From 11bf7a9b8e9f2a96038998a3c9221228e90874aa Mon Sep 17 00:00:00 2001 From: jsboige Date: Sat, 10 Oct 2026 16:18:40 +0200 Subject: [PATCH 1/6] refactor(serie,#17417): renomme 01-PythonForDataScience -> 01-Python-For-Data-Science (T2-01) Rename kebab pre-autorise par le plan de mapping #20273 (porte libre, precedent 04b-Wavelet-Scattering). - git mv seul (13 fichiers, historique preserve) - 21 referents vivants mis a jour (workflows, pytest.ini, _quarto.yml, READMEs, curriculum, baselines nav-chain + pedagogy density, guides) - COURSE_CATALOG.generated.* byte-identique a main (automatisation) - translations/** intact (garde #20232) - 5 fichiers historiques preserves (migration terminee, _archive/, ledgers dates) Organes: nav-chain 0, navlinks 0, label-agreement 16=16 base-inherited, link-render 0, docs-links 0/8536, quarto 0 manquant, testpaths 0, density orphans 0, tests 112 passed. See #17417, See #20273 Co-Authored-By: Claude Sonnet 5.5 --- .github/workflows/genai-helpers-tests.yml | 2 +- .github/workflows/scripts-tests.yml | 8 +- .../README.md | 2 +- .../data/PROVENANCE.md | 0 .../data/penguins.csv | 0 .../data/penguins_raw.csv | 0 .../1.1-Python_pour_la_Data_Science.ipynb | 0 ...2-Manipulation_de_Donnees_avec_NumPy.ipynb | 0 .../1.3-Analyse_de_Donnees_avec_Pandas.ipynb | 0 ...1.4-Visualisation_Matplotlib_Seaborn.ipynb | 0 ...xploration_Nettoyage_Donnees_Reelles.ipynb | 0 .../1.6-Statistiques_Descriptives.ipynb | 0 .../tests/test_numpy_basics.py | 0 .../tests/test_pandas_basics.py | 0 .../tests/test_penguins_cleaning.py | 0 .../02-ML-Cours/2.1-Workflow-ML.ipynb | 4 +- .../02-ML-Cours/README.md | 6 +- .../03-DeepLearning/README.md | 2 +- .../DataScienceWithAgents/04-Vision/README.md | 2 +- .../ML/DataScienceWithAgents/README.md | 8 +- .../Track1-LangChain/README.md | 4 +- MyIA.AI.Notebooks/ML/README.md | 10 +- _quarto.yml | 14 +- docs/curriculum/ml-engineer.compiled.json | 4 +- docs/curriculum/ml-engineer.json | 4 +- docs/curriculum/trading.md | 4 +- docs/reference/notebook-enricher-guide.md | 484 +- docs/reference/quant-prose-census.md | 2 +- pytest.ini | 2 +- scripts/check_testpaths_coverage.py | 2 +- .../pedagogy_density_baseline.json | 4 +- .../tests/test_scan_quant_classify.py | 4 +- scripts/tests/baseline_nb_nav_chain.json | 4034 ++++++++--------- 33 files changed, 2303 insertions(+), 2303 deletions(-) rename MyIA.AI.Notebooks/ML/DataScienceWithAgents/{01-PythonForDataScience => 01-Python-For-Data-Science}/README.md (98%) rename MyIA.AI.Notebooks/ML/DataScienceWithAgents/{01-PythonForDataScience => 01-Python-For-Data-Science}/data/PROVENANCE.md (100%) rename MyIA.AI.Notebooks/ML/DataScienceWithAgents/{01-PythonForDataScience => 01-Python-For-Data-Science}/data/penguins.csv (100%) rename MyIA.AI.Notebooks/ML/DataScienceWithAgents/{01-PythonForDataScience => 01-Python-For-Data-Science}/data/penguins_raw.csv (100%) rename MyIA.AI.Notebooks/ML/DataScienceWithAgents/{01-PythonForDataScience => 01-Python-For-Data-Science}/notebooks/1.1-Python_pour_la_Data_Science.ipynb (100%) rename MyIA.AI.Notebooks/ML/DataScienceWithAgents/{01-PythonForDataScience => 01-Python-For-Data-Science}/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb (100%) rename MyIA.AI.Notebooks/ML/DataScienceWithAgents/{01-PythonForDataScience => 01-Python-For-Data-Science}/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb (100%) rename MyIA.AI.Notebooks/ML/DataScienceWithAgents/{01-PythonForDataScience => 01-Python-For-Data-Science}/notebooks/1.4-Visualisation_Matplotlib_Seaborn.ipynb (100%) rename MyIA.AI.Notebooks/ML/DataScienceWithAgents/{01-PythonForDataScience => 01-Python-For-Data-Science}/notebooks/1.5-Exploration_Nettoyage_Donnees_Reelles.ipynb (100%) rename MyIA.AI.Notebooks/ML/DataScienceWithAgents/{01-PythonForDataScience => 01-Python-For-Data-Science}/notebooks/1.6-Statistiques_Descriptives.ipynb (100%) rename MyIA.AI.Notebooks/ML/DataScienceWithAgents/{01-PythonForDataScience => 01-Python-For-Data-Science}/tests/test_numpy_basics.py (100%) rename MyIA.AI.Notebooks/ML/DataScienceWithAgents/{01-PythonForDataScience => 01-Python-For-Data-Science}/tests/test_pandas_basics.py (100%) rename MyIA.AI.Notebooks/ML/DataScienceWithAgents/{01-PythonForDataScience => 01-Python-For-Data-Science}/tests/test_penguins_cleaning.py (100%) diff --git a/.github/workflows/genai-helpers-tests.yml b/.github/workflows/genai-helpers-tests.yml index 1b31f6f39b..32495900d2 100644 --- a/.github/workflows/genai-helpers-tests.yml +++ b/.github/workflows/genai-helpers-tests.yml @@ -29,7 +29,7 @@ name: GenAI-Helpers-Tests # 3. pytest.ini gagne le testpath correspondant (découverte locale # alignée sur la CI). # 4. Les autres familles SANS trigger path-reactif (#13746 : GradeBookApp, -# 01-PythonForDataScience/tests, QuantConnect/scripts/tests) restent +# 01-Python-For-Data-Science/tests, QuantConnect/scripts/tests) restent # HORS de cette PR — une famille par PR (G.4 anti-composite). NB : ces # trois familles SONT exécutées par scripts-tests.yml (cibles du run # + collect floors) ; ce qui leur manque est une entree paths: propre diff --git a/.github/workflows/scripts-tests.yml b/.github/workflows/scripts-tests.yml index b05db358dd..9250f3a35f 100644 --- a/.github/workflows/scripts-tests.yml +++ b/.github/workflows/scripts-tests.yml @@ -118,7 +118,7 @@ on: # #13746 tranche 2 : les trois suites executees par le bloc pytest # ci-dessous qui ne vivent PAS sous scripts/** -- GradeBookApp, # QuantConnect/scripts/tests (imports : audit_readme, - # validate_qc_notebooks, test_algorithms) et 01-PythonForDataScience/ + # validate_qc_notebooks, test_algorithms) et 01-Python-For-Data-Science/ # tests (numpy/pandas purs, tests self-contenus sans reference aux # notebooks) -- n'avaient aucun declencheur sur leurs propres arbres : # elles ne s'executaient qu'en riders d'un push scripts/**, et une PR @@ -129,7 +129,7 @@ on: # Python (tests self-contenus), et GradeBookApp/** qui est la cible # pytest verbatim. Miroir push/pull_request. - 'MyIA.AI.Notebooks/QuantConnect/scripts/**' - - 'MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/tests/**' + - 'MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/tests/**' - 'GradeBookApp/**' pull_request: branches: [main] @@ -183,7 +183,7 @@ on: - 'MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/tests/**' # #13746 tranche 2 (mirror of the push paths above). - 'MyIA.AI.Notebooks/QuantConnect/scripts/**' - - 'MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/tests/**' + - 'MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/tests/**' - 'GradeBookApp/**' workflow_dispatch: @@ -412,7 +412,7 @@ jobs: MyIA.AI.Notebooks/SymbolicAI/Lean/agent_tests/tests/test_prover_forensic_guards.py \ MyIA.AI.Notebooks/SymbolicAI/Lean/agent_tests/tests/test_provider_gate_18709.py \ MyIA.AI.Notebooks/GenAI/Audio/04-Applications/v4/prosody_lab/test_bake_bank.py \ - MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/tests \ + MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/tests \ scripts/secrets/tests \ scripts/quantconnect/tests \ MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis \ diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/README.md b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/README.md similarity index 98% rename from MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/README.md rename to MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/README.md index b33b0bfa5b..a4451c6cb6 100644 --- a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/README.md +++ b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/README.md @@ -1,4 +1,4 @@ -# 01-PythonForDataScience — Fondations NumPy & Pandas +# 01-Python-For-Data-Science — Fondations NumPy & Pandas [← DataScienceWithAgents (parent)](../README.md) | [02-ML-Cours (suite) →](../02-ML-Cours/README.md) diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/data/PROVENANCE.md b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/data/PROVENANCE.md similarity index 100% rename from MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/data/PROVENANCE.md rename to MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/data/PROVENANCE.md diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/data/penguins.csv b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/data/penguins.csv similarity index 100% rename from MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/data/penguins.csv rename to MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/data/penguins.csv diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/data/penguins_raw.csv b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/data/penguins_raw.csv similarity index 100% rename from MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/data/penguins_raw.csv rename to MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/data/penguins_raw.csv diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.1-Python_pour_la_Data_Science.ipynb b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.1-Python_pour_la_Data_Science.ipynb similarity index 100% rename from MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.1-Python_pour_la_Data_Science.ipynb rename to MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.1-Python_pour_la_Data_Science.ipynb diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb similarity index 100% rename from MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb rename to MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb similarity index 100% rename from MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb rename to MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.4-Visualisation_Matplotlib_Seaborn.ipynb b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.4-Visualisation_Matplotlib_Seaborn.ipynb similarity index 100% rename from MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.4-Visualisation_Matplotlib_Seaborn.ipynb rename to MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.4-Visualisation_Matplotlib_Seaborn.ipynb diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.5-Exploration_Nettoyage_Donnees_Reelles.ipynb b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.5-Exploration_Nettoyage_Donnees_Reelles.ipynb similarity index 100% rename from MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.5-Exploration_Nettoyage_Donnees_Reelles.ipynb rename to MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.5-Exploration_Nettoyage_Donnees_Reelles.ipynb diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.6-Statistiques_Descriptives.ipynb b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.6-Statistiques_Descriptives.ipynb similarity index 100% rename from MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.6-Statistiques_Descriptives.ipynb rename to MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.6-Statistiques_Descriptives.ipynb diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/tests/test_numpy_basics.py b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/tests/test_numpy_basics.py similarity index 100% rename from MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/tests/test_numpy_basics.py rename to MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/tests/test_numpy_basics.py diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/tests/test_pandas_basics.py b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/tests/test_pandas_basics.py similarity index 100% rename from MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/tests/test_pandas_basics.py rename to MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/tests/test_pandas_basics.py diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/tests/test_penguins_cleaning.py b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/tests/test_penguins_cleaning.py similarity index 100% rename from MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/tests/test_penguins_cleaning.py rename to MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/tests/test_penguins_cleaning.py diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.1-Workflow-ML.ipynb b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.1-Workflow-ML.ipynb index bf2da11c16..d19d742619 100644 --- a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.1-Workflow-ML.ipynb +++ b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.1-Workflow-ML.ipynb @@ -16,7 +16,7 @@ "source": [ "# 2.1 — Le workflow d'apprentissage automatique\n", "\n", - "**Navigation** : [<< 01-PythonForDataScience](../01-PythonForDataScience/) | [2.2-Descente-de-gradient >>](2.2-Descente-de-gradient.ipynb) | [Index](../README.md)\n", + "**Navigation** : [<< 01-Python-For-Data-Science](../01-Python-For-Data-Science/) | [2.2-Descente-de-gradient >>](2.2-Descente-de-gradient.ipynb) | [Index](../README.md)\n", "\n", "**Kernel** : Python 3\n", "\n", @@ -33,7 +33,7 @@ "\n", "### Prérequis\n", "\n", - "- NumPy et Pandas (voir `01-PythonForDataScience`).\n", + "- NumPy et Pandas (voir `01-Python-For-Data-Science`).\n", "- Bases de Python (boucles, fonctions, indexation).\n", "\n", "> **Référence.** Mitchell, T. (1997), *Machine Learning*, McGraw-Hill. La distinction entraînement/généralisation et le surapprentissage (overfitting) y sont définis comme le concept central de l'apprentissage supervisé." diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/README.md b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/README.md index 2afe5d0e57..998f58a825 100644 --- a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/README.md +++ b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/README.md @@ -1,6 +1,6 @@ # 02-ML-Cours — Le socle Machine Learning canonique avec scikit-learn -[← DataScienceWithAgents (série parente)](../README.md) | [01-PythonForDataScience (prérequis) →](../01-PythonForDataScience/README.md) +[← DataScienceWithAgents (série parente)](../README.md) | [01-Python-For-Data-Science (prérequis) →](../01-Python-For-Data-Science/README.md) **Kernel** : Python 3 · **Bibliothèque** : scikit-learn · **Niveau** : intermédiaire (post NumPy/Pandas) @@ -8,7 +8,7 @@ ## Pourquoi cette série -La formation `DataScienceWithAgents` saute aujourd'hui un maillon. Après les fondations NumPy/Pandas ([`01-PythonForDataScience`](../01-PythonForDataScience/)), les *labs agentic* (LangChain, Google ADK) demandent à des agents LLM de produire et d'exécuter du code de data science — y compris du machine learning. Mais entre les deux, **aucun notebook n'enseigne le workflow ML, un modèle ou une métrique comme un sujet en soi** : scikit-learn n'apparaît que comme une séquence magique non expliquée (un `fit()` isolé dans un lab de visualisation, ou cité en litteral dans une chaîne LLM). +La formation `DataScienceWithAgents` saute aujourd'hui un maillon. Après les fondations NumPy/Pandas ([`01-Python-For-Data-Science`](../01-Python-For-Data-Science/)), les *labs agentic* (LangChain, Google ADK) demandent à des agents LLM de produire et d'exécuter du code de data science — y compris du machine learning. Mais entre les deux, **aucun notebook n'enseigne le workflow ML, un modèle ou une métrique comme un sujet en soi** : scikit-learn n'apparaît que comme une séquence magique non expliquée (un `fit()` isolé dans un lab de visualisation, ou cité en litteral dans une chaîne LLM). Cette série comble ce socle manquant. Elle pose, **à la main et de façon canonique**, les huit chapitres fondamentaux du machine learning supervisé et non supervisé — le référent qui rend *jugeable* ce qu'un agent produira ensuite. L'arc pédagogique suit la progression classique : le **workflow** d'ensemble, puis on ouvre les boîtes noires (**descente de gradient**, **fonction de lien**), on élargit la famille de modèles (**régression linéaire/logistique**, **arbres et ensembles**, **SVM à noyau et k plus proches voisins**), on formalise l'évaluation (**biais-variance, validation croisée, ROC, calibration des probabilités**), puis l'on bascule en **non supervisé** (**clustering, ACP**), avant de clore par le **cadre théorique** (**théorie PAC, dimension VC**). Chaque notebook rend visible un concept-phare — le surapprentissage, la divergence d'un learning rate, la frontière de décision, la réduction de variance, le coût d'un seuil, le kernel trick, la structure retrouvée sans étiquettes, et le nombre d'exemples suffisant pour généraliser. @@ -207,7 +207,7 @@ Chaque notebook suit les mêmes conventions : ## Prérequis -- **NumPy et Pandas** : manipulation de tableaux et DataFrames ([`01-PythonForDataScience`](../01-PythonForDataScience/README.md)). +- **NumPy et Pandas** : manipulation de tableaux et DataFrames ([`01-Python-For-Data-Science`](../01-Python-For-Data-Science/README.md)). - Notions de base : fonction, dérivée, variance, probabilité. ## Suite logique diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/README.md b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/README.md index 284608beb6..f2cb25ae9b 100644 --- a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/README.md +++ b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/README.md @@ -104,7 +104,7 @@ Cette série ancre ses carnets dans la **référence canonique du deep learning* gradient)](../02-ML-Cours/2.2-Descente-de-gradient.html), [2.8 (théorie PAC, la borne que ce notebook confronte)](../02-ML-Cours/2.8-Theorie-PAC.html) et [2.9 (le grokking boîte noire que le 3.5 rouvre à la main)](../02-ML-Cours/2.9-Grokking-Generalisation.html) -- NumPy niveau [1.2](../01-PythonForDataScience/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.html) +- NumPy niveau [1.2](../01-Python-For-Data-Science/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.html) (produits matriciels, broadcast) ## Environnement diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/README.md b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/README.md index db2bd5cad6..7d39a264f4 100644 --- a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/README.md +++ b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/README.md @@ -46,7 +46,7 @@ Cette série suit la **même discipline que 03** : *from scratch PUIS framework, ## Prérequis - [`03-DeepLearning`](../03-DeepLearning/README.md) en entier — en particulier [3.4 (Attention-Transformer from scratch)](../03-DeepLearning/3.4-Attention-Transformer-From-Scratch.html) qui partage le mécanisme résiduel. -- NumPy niveau [`01-PythonForDataScience/1.2`](../01-PythonForDataScience/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.html) (broadcast, produits matriciels). +- NumPy niveau [`01-Python-For-Data-Science/1.2`](../01-Python-For-Data-Science/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.html) (broadcast, produits matriciels). - PyTorch CPU (installé via `pip install torch torchvision`). ## Environnement diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/README.md b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/README.md index 3edceda6f3..1798e5dae3 100644 --- a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/README.md +++ b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/README.md @@ -74,7 +74,7 @@ Labs 1 + 6 + 8. Découvrir le pipeline data science, construire un premier agent ``` DataScienceWithAgents/ -├── 01-PythonForDataScience/ # Fondations Python +├── 01-Python-For-Data-Science/ # Fondations Python │ └── notebooks/ │ ├── 1.2-NumPy.ipynb │ └── 1.3-Pandas.ipynb @@ -140,12 +140,12 @@ DataScienceWithAgents/ └── Day7-Production/ # Integration GCP ``` -## Fondations (01-PythonForDataScience) +## Fondations (01-Python-For-Data-Science) | Notebook | Contenu | Durée | |----------|---------|-------| -| [1.2-NumPy](01-PythonForDataScience/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.html) | Arrays, opérations, vectorisation | 45 min | -| [1.3-Pandas](01-PythonForDataScience/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.html) | DataFrames, filtrage, groupby | 60 min | +| [1.2-NumPy](01-Python-For-Data-Science/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.html) | Arrays, opérations, vectorisation | 45 min | +| [1.3-Pandas](01-Python-For-Data-Science/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.html) | DataFrames, filtrage, groupby | 60 min | ## Fondations ML (02-ML-Cours) diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/README.md b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/README.md index f9d9a00ac8..efc9fa0237 100644 --- a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/README.md +++ b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/README.md @@ -2,7 +2,7 @@ [← DataScienceWithAgents (parent)](../README.md) | [Track2-GoogleADK (Google ADK) →](../Track2-GoogleADK/README.md) -Le track **LangChain** du workshop : 7 labs répartis sur 3 jours qui mènent de la prise en main des outils data science (Journée 1) jusqu'à la construction d'un agent d'analyse de données autonome (Journée 3). C'est le compagnon opérationnel des fondations [`01-PythonForDataScience`](../01-PythonForDataScience/README.md) et [`02-ML-Cours`](../02-ML-Cours/README.md) : on y apprend à orchestrer un LLM pour qu'il accomplisse des tâches data science concrètes — analyser un appel d'offres, pré-qualifier des CVs, nettoyer des données sales, interroger un DataFrame en langage naturel. +Le track **LangChain** du workshop : 7 labs répartis sur 3 jours qui mènent de la prise en main des outils data science (Journée 1) jusqu'à la construction d'un agent d'analyse de données autonome (Journée 3). C'est le compagnon opérationnel des fondations [`01-Python-For-Data-Science`](../01-Python-For-Data-Science/README.md) et [`02-ML-Cours`](../02-ML-Cours/README.md) : on y apprend à orchestrer un LLM pour qu'il accomplisse des tâches data science concrètes — analyser un appel d'offres, pré-qualifier des CVs, nettoyer des données sales, interroger un DataFrame en langage naturel. ## Pourquoi cette série @@ -45,7 +45,7 @@ Chaque lab est accompagné de son propre [`README.md`](Day1-Foundations/Labs/REA ## Prérequis -- Fondations [`01-PythonForDataScience`](../01-PythonForDataScience/README.md) (NumPy/Pandas) et idéalement [`02-ML-Cours`](../02-ML-Cours/README.md) (workflow sklearn). +- Fondations [`01-Python-For-Data-Science`](../01-Python-For-Data-Science/README.md) (NumPy/Pandas) et idéalement [`02-ML-Cours`](../02-ML-Cours/README.md) (workflow sklearn). - **LangChain** + clé API OpenAI (les labs 2, 3, 6, 7 appellent un LLM). ## Suite logique diff --git a/MyIA.AI.Notebooks/ML/README.md b/MyIA.AI.Notebooks/ML/README.md index bf8bae56d3..147df7d153 100644 --- a/MyIA.AI.Notebooks/ML/README.md +++ b/MyIA.AI.Notebooks/ML/README.md @@ -68,7 +68,7 @@ Le parcours Python s'articule en trois temps. Les **fondations** (NumPy/Pandas) ### Parcours Data Scientist classique (~12h) 1. [ML-01](ML.Net/ML-01-Introduction.html) → comprendre le pipeline ML -1. [NumPy](DataScienceWithAgents/01-PythonForDataScience/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb) + [Pandas](DataScienceWithAgents/01-PythonForDataScience/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb) → maîtriser les données +1. [NumPy](DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb) + [Pandas](DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb) → maîtriser les données 1. [Socle ML canonique 2.1 → 2.8](DataScienceWithAgents/02-ML-Cours/) → les fondamentaux scikit-learn (gradient, régressions, ensembles, biais-variance, non supervisé, PAC) 1. [ML-02](ML.Net/ML-02-Data&Features.html) + [ML-03](ML.Net/ML-03-Entrainement&AutoML.html) → entraîner des modèles 1. [ML-04](ML.Net/ML-04-Evaluation.html) → évaluer rigoureusement @@ -122,7 +122,7 @@ ML/ │ └── taxi-fare.csv │ ├── DataScienceWithAgents/ # Data Science Python + AI Agents -│ ├── 01-PythonForDataScience/ # Fondations NumPy/Pandas +│ ├── 01-Python-For-Data-Science/ # Fondations NumPy/Pandas │ ├── 02-ML-Cours/ # Socle ML canonique (scikit-learn) │ │ ├── 2.8b-Theorie-PAC-Lean.ipynb # compagnon Lean (lean4-wsl) du lake learning_theory_lean (moitie PAC) │ │ └── 2.8d-Lean-Novikoff-Convergence.ipynb # compagnon Lean (lean4-wsl) du lake learning_theory_lean (moitie Perceptron) @@ -224,12 +224,12 @@ L'écart entre le pire et le meilleur essai est de **338×** — un fait pédago Formation complète en Data Science Python enrichie d'agents IA. Vous commencerez par les fondamentaux (NumPy, Pandas), puis construirez des agents LLM capables d'analyser des données, de nettoyer des datasets, et de participer à des compétitions Kaggle. La seconde moitié plonge dans les frameworks Google ADK pour construire des systèmes multi-agents avancés. -### Fondations (01-PythonForDataScience) +### Fondations (01-Python-For-Data-Science) | Notebook | Contenu | |----------|---------| -| [1.2-NumPy](DataScienceWithAgents/01-PythonForDataScience/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb) | Arrays, vectorisation, opérations | -| [1.3-Pandas](DataScienceWithAgents/01-PythonForDataScience/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb) | DataFrames, filtrage, manipulation | +| [1.2-NumPy](DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb) | Arrays, vectorisation, opérations | +| [1.3-Pandas](DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb) | DataFrames, filtrage, manipulation | #### Trois figures du track Data Science (Pandas, Seaborn, ADK) diff --git a/_quarto.yml b/_quarto.yml index 98aa2a70fb..0ac4830371 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -209,7 +209,7 @@ project: - "MyIA.AI.Notebooks/GenAI/Video/README.md" - "MyIA.AI.Notebooks/IIT/ICT-Series/README.md" - "MyIA.AI.Notebooks/IIT/README.md" - - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/README.md" + - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/README.md" - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/README.md" - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/README.md" - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/README.md" @@ -1222,12 +1222,12 @@ project: - "MyIA.AI.Notebooks/IIT/IIT-04-Le-Probleme-de-Frontiere.ipynb" - "MyIA.AI.Notebooks/IIT/IIT-05-Lentilles-et-Dissociations.ipynb" - "MyIA.AI.Notebooks/IIT/IIT-06-L-Objet-qui-a-Mordu-IIT.ipynb" - - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.1-Python_pour_la_Data_Science.ipynb" - - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb" - - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb" - - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.4-Visualisation_Matplotlib_Seaborn.ipynb" - - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.5-Exploration_Nettoyage_Donnees_Reelles.ipynb" - - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.6-Statistiques_Descriptives.ipynb" + - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.1-Python_pour_la_Data_Science.ipynb" + - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb" + - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb" + - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.4-Visualisation_Matplotlib_Seaborn.ipynb" + - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.5-Exploration_Nettoyage_Donnees_Reelles.ipynb" + - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.6-Statistiques_Descriptives.ipynb" - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.1-Workflow-ML.ipynb" - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.10-Optimisation-Hyperparametres.ipynb" - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.11-Regularisation-Sparse-LASSO.ipynb" diff --git a/docs/curriculum/ml-engineer.compiled.json b/docs/curriculum/ml-engineer.compiled.json index 1bf950b7fc..1b5e37d11d 100644 --- a/docs/curriculum/ml-engineer.compiled.json +++ b/docs/curriculum/ml-engineer.compiled.json @@ -6,7 +6,7 @@ "prerequisites": [], "notebooks": [ { - "path": "ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb", + "path": "ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb", "title": "1.2 - Manipulation de Données avec NumPy", "duration_minutes": 45, "kernel": "Python 3", @@ -19,7 +19,7 @@ } }, { - "path": "ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb", + "path": "ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb", "title": "1.3 - Analyse de Données avec Pandas", "duration_minutes": 30, "kernel": "Python 3", diff --git a/docs/curriculum/ml-engineer.json b/docs/curriculum/ml-engineer.json index 773233163b..e205dc4263 100644 --- a/docs/curriculum/ml-engineer.json +++ b/docs/curriculum/ml-engineer.json @@ -5,8 +5,8 @@ "id": "donnees", "description": "Manipulation de donnees en Python : structures numeriques (NumPy) et tabulaires (Pandas). Le ML Engineer passe 80 % de son temps a preparer les donnees ; ce socle est non negociable avant tout modele. Le pre-requis data science du pli 2 (agentification Pandas/LangChain) releve d'un autre grain.", "notebooks": [ - "ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb", - "ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb" + "ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb", + "ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb" ] }, { diff --git a/docs/curriculum/trading.md b/docs/curriculum/trading.md index 281a5ff0c8..6453f3ada3 100644 --- a/docs/curriculum/trading.md +++ b/docs/curriculum/trading.md @@ -29,8 +29,8 @@ Stratégies de trading algorithmique avec QuantConnect, pipeline ML (Transformer | # | Notebook | Maturité | Exécutable | |---|----------|----------|------------| -| 1 | [1.2 - Manipulation de Données avec NumPy](../../MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb) | BETA | Oui | -| 2 | [1.3 - Analyse de Données avec Pandas](../../MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb) | BETA | Oui | +| 1 | [1.2 - Manipulation de Données avec NumPy](../../MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb) | BETA | Oui | +| 2 | [1.3 - Analyse de Données avec Pandas](../../MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb) | BETA | Oui | | 3 | [2.1 — Le workflow d'apprentissage automatique](../../MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.1-Workflow-ML.ipynb) | BETA | Oui | | 4 | [2.10 — Optimisation d'hyperparamètres : grille, hasard,…](../../MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.10-Optimisation-Hyperparametres.ipynb) | BETA | Non | | 5 | [2.11 — Régularisation sparse : LASSO (L1) vs Ridge…](../../MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.11-Regularisation-Sparse-LASSO.ipynb) | BETA | Oui | diff --git a/docs/reference/notebook-enricher-guide.md b/docs/reference/notebook-enricher-guide.md index 160c5c7e20..87785a86e2 100644 --- a/docs/reference/notebook-enricher-guide.md +++ b/docs/reference/notebook-enricher-guide.md @@ -1,242 +1,242 @@ -# Guide d'enrichissement des notebooks — méthodologie & vocabulaire par domaine - - - -> **Provenance** : consolidé depuis `.claude/agent-memory/notebook-enricher/MEMORY.md` (relocalisé ici le 2026-08-10, item-7 de #9535). Ce fichier regroupe les leçons trans-machine de l'agent `notebook-enricher` : règles de positionnement des cellules, vocabulaire pédagogique par famille, patrons de contenu et checklist qualité. Il complète [.claude/rules/notebook-conventions.md](../../.claude/rules/notebook-conventions.md) (règles C.1/C.2/C.3) et [docs/reference/procedures-recurrentes.md](procedures-recurrentes.md) (workflow d'enrichissement). -> -> **Note de fraîcheur** : les « session logs » datés (2026-02 à 2026-06) sont conservés comme provenance historique ; les références de fichiers `enrichment-log-*.md` / `enrichment_summary_*.md` pointent vers des journaux de session locaux non conservés dans le dépôt. Les noms de notebooks cités peuvent avoir évolué (renumérotation #5081). - - - -## Règles de positionnement des cellules (CRITIQUE) - - - -### Cell Positioning Rules (CRITICAL) - - - -**Golden Rule**: Work BOTTOM to TOP to avoid index shifting during insertions. - - - -**Verification**: After each insertion batch, re-read the notebook to confirm cell_id references. - - - -**Never**: Insert cells before re-reading when doing multiple insertions in one notebook. - - - -### Successful Enrichment Sessions - - - -- **2026-02-15**: QC-Py-24-Autoencoders-Anomaly (11 cells added, 7.5/10 → 9.0/10) - - VAE + HMM for anomaly detection and regime switching - - BOTTOM-to-TOP insertion strategy flawless (11 cells, no errors) - - Fixed code/markdown ratio from 62/38 to 43/57 - - All interpretations positioned correctly after code outputs - - Added interpretations for: data structure, features, VAE training, anomaly detection, VAE comparison, transition matrix, HMM comparison, regime characterization, backtest, model sizes, QC production code - - Critical transition cell between VAE and HMM sections - - Summary report: `enrichment_summary_qc24.md` - - - -- **2026-02-15**: QC-Py-23-Attention-Transformers (11 cells added, 7/10 → 8.5/10) - - Advanced ML/SSM content with PyTorch implementations - - BOTTOM-to-TOP insertion strategy flawless (11 cells, no errors) - - Fixed code/markdown ratio from 62/38 to 43/57 - - All interpretations positioned correctly after code outputs - - Added interpretations for: complexity viz, SSM demo, S4 test, Mamba test, training results, SST hybrid, benchmark - - Summary report: `enrichment_summary_qc23.md` - - - -- **2026-02-07**: DataScienceWithAgents Labs (3 notebooks, 11 cells added) — journal de session local `enrichment-log-2026-02-07.md` (non conservé dans le dépôt) - - All cells positioned correctly on first attempt - - BOTTOM-to-TOP strategy prevented index conflicts - - No git rollbacks needed -- **2026-02-16**: Sudoku-10-ORTools (anciennement « Sudoku-03-ORTools » avant la renumérotation #5081 ; 4 cells added: 1 header with objectives, 2 interpretations, 1 footer) - - Navigation header and footer with Search notebook links - - Learning objectives (Bloom taxonomy) for CSP, CP-SAT, MIP - - Duration: 50 minutes, Prerequisites: Sudoku-01-Backtracking - - Interpretations after CP solver test and performance comparison - - BOTTOM-to-TOP strategy with re-read between each insertion -- **2026-02-16**: Search — notebook métaheuristiques (anciennement « Search-9-Metaheuristics » avant la renumérotation #5081 ; 3 cells added: 2 interpretations, 1 code improvement) - - Added interpretation after parameter analysis visualization (pop_size impact) - - Added interpretation after PSO convergence visualization with technical note - - Replaced seaborn with matplotlib in comparative plots (removed dependency) - - All cells positioned correctly, BOTTOM-to-TOP strategy used -- **2026-02-19**: Video GPU Notebooks Pedagogical Enhancement (6 notebooks) - - Replaced "désactivé/non disponible" messages with detailed pedagogical outputs - - Added MODE PEDAGOGIQUE sections with expected parameters, results, and reproduction code - - - Notebooks: 01-3-Qwen-VL, 01-4-ESRGAN, 02-1-HunyuanVideo, 02-2-LTX-Video, 02-3-Wan, 02-4-SVD - - Used edit_mode="replace" on existing interpretation cells (not insertions) - - No cell positioning issues since we replaced existing cells -- **2026-03-16**: GameTheory Series Header Standardization (12 notebooks reviewed, 6 fixed) - - Systematic review of all 17 GameTheory notebooks for header compliance - - Fixed pattern: "**Objectifs pedagogiques** :" (bold) → "### Objectifs d'apprentissage" (header) - - Fixed pattern: "### Objectifs pedagogiques" → "### Objectifs d'apprentissage" - - Fixed pattern: "**Prerequis**" / "**Duree estimee**" bold → proper ### headers - - Notebooks fixed: 8, 11, 12, 13, 14, 15, 16, 16b, 17 (across two sessions) - - Notebooks already correct: 1, 2, 4c, 8c, 15b, 15c, 16c - - Lean notebooks (4b, 16b): only markdown cells modified, never code/lean cells - - Also fixed wrong navigation links: notebook-11 pointing to itself instead of notebook-12 - - GameTheory domain: CFR, Stackelberg, Fictitious Play, PSRO, AlphaZero, Arrow/Sen, Sprague-Grundy - - Key rule confirmed: Lean notebooks follow same header standards as Python notebooks - - - -- **2026-06-03**: DataScienceWithAgents Exercise Stubs (8 notebooks, 17 exercise pairs added for >=3 convention, See #2161) - - Added exercise stubs (markdown context + code cell) to meet >=3 exercises per notebook - - Notebooks: Lab2-RFP (+2), Lab3-CV (+2), Lab4-DataWrangling (+1), Lab5-Viz-ML (+2), Lab6-First-Agent (+3), Lab7-Data-Analysis-Agent (+2), 1.2-NumPy (+1), 1.3-Pandas (+3) - - All stubs use `None` / `pass` pattern with `# Etape N` and `# Indice` comments - - Each exercise preceded by markdown cell with objective + indices - - No `raise NotImplementedError` / `assert False` / `1/0` used - - Path correction: NumPy/Pandas notebooks are under `01-PythonForDataScience/` not `Track1-LangChain/` - - - -- **2026-03-03**: Lean-11-TorchLean Pedagogical Enhancement (4 cells added, SQUELETTE → COMPLET) - - TorchLean: réseaux de neurones formellement vérifiés avec Lean 4 - - BOTTOM-to-TOP insertion strategy flawless (4 cells, no errors) - - Added sections: 3.0 (API philosophy), 4.5 (numerical errors), 5.4 (IBP visualization), 8.6 (visual synthesis) - - Rich pedagogical content: API comparison PyTorch vs TorchLean, Float32 error accumulation examples, complete IBP propagation with ASCII diagrams, ecosystem workflow visualization - - Domain vocabulary: Semantic gap, IEEE-754 rounding modes (RNE, RTZ, RTP, RTN, RNA), ULP, IBP, CROWN, LiRPA, PINNs, Lyapunov control, FloVerCoq - - README status updated from SQUELETTE to COMPLET - - Summary report: `enrichment-log-2026-03-03-lean-torchlean.md` - - - -### Domain-Specific Patterns - - - -| Domain | Key Vocabulary | Common Patterns | -|--------|----------------|-----------------| -| Data Science with Agents | Pandas, LangChain, DataFrame, agent, tools, reasoning | Progressive questions (simple->complex), data cleaning workflow, agent orchestration | -| ML | accuracy, loss, overfitting, cross-validation | Train-test split, model evaluation, interpretation tables | -| ML Advanced (SSM/Transformers) | State Space Models, Mamba, S4, selective, complexity O(n), HiPPO, discretization | Architecture comparisons, complexity analysis, training curves, parameter counts | -| ML Advanced (VAE/HMM) | VAE, reconstruction error, KL divergence, beta-VAE, HMM, Viterbi, transition matrix, regime switching | Model comparison tables, anomaly detection thresholds, regime characterization, production deployment | -| Probas | prior, posterior, likelihood, inference | Bayesian updates, factor graphs, distribution visualization | -| Sudoku/Constraint Solving | CSP, CP-SAT, MIP, DecisionBuilder, AllDifferent, propagation | Solver comparison tables, performance benchmarks, constraint modeling patterns | -| Search/Optimization | fitness, convergence, population, exploration/exploitation, metaheuristics | Parameter sensitivity analysis, algorithm comparison tables, convergence plots | -| QuantConnect | backtest, alpha model, features, signals, ObjectStore | Model training → save → deploy, feature engineering, performance metrics | -| SymbolicAI/TorchLean | Semantic gap, Float32, IEEE-754, rounding modes, IBP, CROWN, LiRPA, PINNs, Lyapunov, FloVerCoq | API philosophy, numerical error accumulation, interval propagation visualization, ecosystem diagrams | -| GameTheory | Nash equilibrium, Stackelberg, CFR, Fictitious Play, PSRO, AlphaZero, Sprague-Grundy, Nim, Arrow impossibility, Sen paradox, MARL, self-play | Convergence plots, exploitability tables, strategy evolution, P/N-position analysis | - - - -### Content Strategy Templates - - - -**Introduction Cell** (BEFORE code): -- Future tense ("nous allons...", "le code va...") -- Sets expectations -- Explains "why" before "how" - - - -**Interpretation Cell** (AFTER code): -- Past/present tense ("le resultat montre...", "on observe...") -- Tables for structured data -- > **Note technique** for important details - - - -**Transition Cell** (BETWEEN sections): -- Links concepts -- Previews next steps -- Maintains pedagogical flow - - - -### Errors to Avoid - - - -1. **Never** insert interpretation BEFORE the code it analyzes -2. **Never** skip re-reading after insertions -3. **Never** use ad-hoc Python for notebook manipulation (use notebook_helpers.py) -4. **Never** add emojis -5. **Never** modify existing code cells (l'enrichissement ajoute du markdown autour du code existant ; pour corriger une cellule code cassée, tracer une PR séparée — cf [anti-regression.md](../../.claude/rules/anti-regression.md)) - - - -### Quality Checklist - - - -Before completing enrichment: -- [ ] No consecutive code cells without markdown -- [ ] All notebooks have learning objectives header -- [ ] Prerequisites and duration specified -- [ ] Interpretation cells are AFTER code cells -- [ ] Git diff shows more insertions than deletions -- [ ] Professional French language (no emojis) -- [ ] Domain vocabulary is accurate - - - -### Tools Reference - - - -L'outil désigné (cf [CLAUDE.md](../../CLAUDE.md) « Catalogue agents / skills / scripts ») est la CLI multi-famille `scripts/notebook_tools/notebook_tools.py` : - - - -```bash -# Valider la structure d'un notebook (cellules, execution_count, outputs) -python scripts/notebook_tools/notebook_tools.py validate - - - -# Analyser les sorties (détecter les erreurs, les outputs vides) -python scripts/notebook_tools/notebook_tools.py analyze - - - -# Extraire le squelette (index de cellules pour le repérage avant insertion) -python scripts/notebook_tools/notebook_tools.py skeleton -``` - - - -L'utilitaire `notebook_helpers.py` reste utile pour un listing détaillé par cellule : - - - -```bash -# Lister les cellules avec leurs indices (repérage avant insertion BOTTOM-to-TOP) -python scripts/notebook_tools/notebook_helpers.py list --verbose - - - -# Vérifier l'absence de cellules code consécutives sans markdown intercalaire -grep -A1 "cell_type.*code" - - - -# Vérifier le diff final -git diff --stat -``` - - - -Catalogue complet des scripts : [scripts-reference.md](scripts-reference.md). - - - -### Documentation associée - - - -- [notebook-conventions.md](../../.claude/rules/notebook-conventions.md) — règles C.1/C.2/C.3 (stubs sans erreur volontaire, outputs commités, scope des re-exécutions). -- [procedures-recurrentes.md](procedures-recurrentes.md) — workflow d'enrichissement et pré-commit notebook (H.3). -- [subagents-reference.md](subagents-reference.md) — catalogue des sous-agents (`notebook-enricher`, `notebook-cleaner`, `notebook-designer`). -- Les journaux de session détaillés (`enrichment-log-YYYY-MM-DD.md`) sont des artefacts locaux hors dépôt ; cette doc n'en conserve que la synthèse trans-machine. - +# Guide d'enrichissement des notebooks — méthodologie & vocabulaire par domaine + + + +> **Provenance** : consolidé depuis `.claude/agent-memory/notebook-enricher/MEMORY.md` (relocalisé ici le 2026-08-10, item-7 de #9535). Ce fichier regroupe les leçons trans-machine de l'agent `notebook-enricher` : règles de positionnement des cellules, vocabulaire pédagogique par famille, patrons de contenu et checklist qualité. Il complète [.claude/rules/notebook-conventions.md](../../.claude/rules/notebook-conventions.md) (règles C.1/C.2/C.3) et [docs/reference/procedures-recurrentes.md](procedures-recurrentes.md) (workflow d'enrichissement). +> +> **Note de fraîcheur** : les « session logs » datés (2026-02 à 2026-06) sont conservés comme provenance historique ; les références de fichiers `enrichment-log-*.md` / `enrichment_summary_*.md` pointent vers des journaux de session locaux non conservés dans le dépôt. Les noms de notebooks cités peuvent avoir évolué (renumérotation #5081). + + + +## Règles de positionnement des cellules (CRITIQUE) + + + +### Cell Positioning Rules (CRITICAL) + + + +**Golden Rule**: Work BOTTOM to TOP to avoid index shifting during insertions. + + + +**Verification**: After each insertion batch, re-read the notebook to confirm cell_id references. + + + +**Never**: Insert cells before re-reading when doing multiple insertions in one notebook. + + + +### Successful Enrichment Sessions + + + +- **2026-02-15**: QC-Py-24-Autoencoders-Anomaly (11 cells added, 7.5/10 → 9.0/10) + - VAE + HMM for anomaly detection and regime switching + - BOTTOM-to-TOP insertion strategy flawless (11 cells, no errors) + - Fixed code/markdown ratio from 62/38 to 43/57 + - All interpretations positioned correctly after code outputs + - Added interpretations for: data structure, features, VAE training, anomaly detection, VAE comparison, transition matrix, HMM comparison, regime characterization, backtest, model sizes, QC production code + - Critical transition cell between VAE and HMM sections + - Summary report: `enrichment_summary_qc24.md` + + + +- **2026-02-15**: QC-Py-23-Attention-Transformers (11 cells added, 7/10 → 8.5/10) + - Advanced ML/SSM content with PyTorch implementations + - BOTTOM-to-TOP insertion strategy flawless (11 cells, no errors) + - Fixed code/markdown ratio from 62/38 to 43/57 + - All interpretations positioned correctly after code outputs + - Added interpretations for: complexity viz, SSM demo, S4 test, Mamba test, training results, SST hybrid, benchmark + - Summary report: `enrichment_summary_qc23.md` + + + +- **2026-02-07**: DataScienceWithAgents Labs (3 notebooks, 11 cells added) — journal de session local `enrichment-log-2026-02-07.md` (non conservé dans le dépôt) + - All cells positioned correctly on first attempt + - BOTTOM-to-TOP strategy prevented index conflicts + - No git rollbacks needed +- **2026-02-16**: Sudoku-10-ORTools (anciennement « Sudoku-03-ORTools » avant la renumérotation #5081 ; 4 cells added: 1 header with objectives, 2 interpretations, 1 footer) + - Navigation header and footer with Search notebook links + - Learning objectives (Bloom taxonomy) for CSP, CP-SAT, MIP + - Duration: 50 minutes, Prerequisites: Sudoku-01-Backtracking + - Interpretations after CP solver test and performance comparison + - BOTTOM-to-TOP strategy with re-read between each insertion +- **2026-02-16**: Search — notebook métaheuristiques (anciennement « Search-9-Metaheuristics » avant la renumérotation #5081 ; 3 cells added: 2 interpretations, 1 code improvement) + - Added interpretation after parameter analysis visualization (pop_size impact) + - Added interpretation after PSO convergence visualization with technical note + - Replaced seaborn with matplotlib in comparative plots (removed dependency) + - All cells positioned correctly, BOTTOM-to-TOP strategy used +- **2026-02-19**: Video GPU Notebooks Pedagogical Enhancement (6 notebooks) + - Replaced "désactivé/non disponible" messages with detailed pedagogical outputs + - Added MODE PEDAGOGIQUE sections with expected parameters, results, and reproduction code + + - Notebooks: 01-3-Qwen-VL, 01-4-ESRGAN, 02-1-HunyuanVideo, 02-2-LTX-Video, 02-3-Wan, 02-4-SVD + - Used edit_mode="replace" on existing interpretation cells (not insertions) + - No cell positioning issues since we replaced existing cells +- **2026-03-16**: GameTheory Series Header Standardization (12 notebooks reviewed, 6 fixed) + - Systematic review of all 17 GameTheory notebooks for header compliance + - Fixed pattern: "**Objectifs pedagogiques** :" (bold) → "### Objectifs d'apprentissage" (header) + - Fixed pattern: "### Objectifs pedagogiques" → "### Objectifs d'apprentissage" + - Fixed pattern: "**Prerequis**" / "**Duree estimee**" bold → proper ### headers + - Notebooks fixed: 8, 11, 12, 13, 14, 15, 16, 16b, 17 (across two sessions) + - Notebooks already correct: 1, 2, 4c, 8c, 15b, 15c, 16c + - Lean notebooks (4b, 16b): only markdown cells modified, never code/lean cells + - Also fixed wrong navigation links: notebook-11 pointing to itself instead of notebook-12 + - GameTheory domain: CFR, Stackelberg, Fictitious Play, PSRO, AlphaZero, Arrow/Sen, Sprague-Grundy + - Key rule confirmed: Lean notebooks follow same header standards as Python notebooks + + + +- **2026-06-03**: DataScienceWithAgents Exercise Stubs (8 notebooks, 17 exercise pairs added for >=3 convention, See #2161) + - Added exercise stubs (markdown context + code cell) to meet >=3 exercises per notebook + - Notebooks: Lab2-RFP (+2), Lab3-CV (+2), Lab4-DataWrangling (+1), Lab5-Viz-ML (+2), Lab6-First-Agent (+3), Lab7-Data-Analysis-Agent (+2), 1.2-NumPy (+1), 1.3-Pandas (+3) + - All stubs use `None` / `pass` pattern with `# Etape N` and `# Indice` comments + - Each exercise preceded by markdown cell with objective + indices + - No `raise NotImplementedError` / `assert False` / `1/0` used + - Path correction: NumPy/Pandas notebooks are under `01-Python-For-Data-Science/` not `Track1-LangChain/` + + + +- **2026-03-03**: Lean-11-TorchLean Pedagogical Enhancement (4 cells added, SQUELETTE → COMPLET) + - TorchLean: réseaux de neurones formellement vérifiés avec Lean 4 + - BOTTOM-to-TOP insertion strategy flawless (4 cells, no errors) + - Added sections: 3.0 (API philosophy), 4.5 (numerical errors), 5.4 (IBP visualization), 8.6 (visual synthesis) + - Rich pedagogical content: API comparison PyTorch vs TorchLean, Float32 error accumulation examples, complete IBP propagation with ASCII diagrams, ecosystem workflow visualization + - Domain vocabulary: Semantic gap, IEEE-754 rounding modes (RNE, RTZ, RTP, RTN, RNA), ULP, IBP, CROWN, LiRPA, PINNs, Lyapunov control, FloVerCoq + - README status updated from SQUELETTE to COMPLET + - Summary report: `enrichment-log-2026-03-03-lean-torchlean.md` + + + +### Domain-Specific Patterns + + + +| Domain | Key Vocabulary | Common Patterns | +|--------|----------------|-----------------| +| Data Science with Agents | Pandas, LangChain, DataFrame, agent, tools, reasoning | Progressive questions (simple->complex), data cleaning workflow, agent orchestration | +| ML | accuracy, loss, overfitting, cross-validation | Train-test split, model evaluation, interpretation tables | +| ML Advanced (SSM/Transformers) | State Space Models, Mamba, S4, selective, complexity O(n), HiPPO, discretization | Architecture comparisons, complexity analysis, training curves, parameter counts | +| ML Advanced (VAE/HMM) | VAE, reconstruction error, KL divergence, beta-VAE, HMM, Viterbi, transition matrix, regime switching | Model comparison tables, anomaly detection thresholds, regime characterization, production deployment | +| Probas | prior, posterior, likelihood, inference | Bayesian updates, factor graphs, distribution visualization | +| Sudoku/Constraint Solving | CSP, CP-SAT, MIP, DecisionBuilder, AllDifferent, propagation | Solver comparison tables, performance benchmarks, constraint modeling patterns | +| Search/Optimization | fitness, convergence, population, exploration/exploitation, metaheuristics | Parameter sensitivity analysis, algorithm comparison tables, convergence plots | +| QuantConnect | backtest, alpha model, features, signals, ObjectStore | Model training → save → deploy, feature engineering, performance metrics | +| SymbolicAI/TorchLean | Semantic gap, Float32, IEEE-754, rounding modes, IBP, CROWN, LiRPA, PINNs, Lyapunov, FloVerCoq | API philosophy, numerical error accumulation, interval propagation visualization, ecosystem diagrams | +| GameTheory | Nash equilibrium, Stackelberg, CFR, Fictitious Play, PSRO, AlphaZero, Sprague-Grundy, Nim, Arrow impossibility, Sen paradox, MARL, self-play | Convergence plots, exploitability tables, strategy evolution, P/N-position analysis | + + + +### Content Strategy Templates + + + +**Introduction Cell** (BEFORE code): +- Future tense ("nous allons...", "le code va...") +- Sets expectations +- Explains "why" before "how" + + + +**Interpretation Cell** (AFTER code): +- Past/present tense ("le resultat montre...", "on observe...") +- Tables for structured data +- > **Note technique** for important details + + + +**Transition Cell** (BETWEEN sections): +- Links concepts +- Previews next steps +- Maintains pedagogical flow + + + +### Errors to Avoid + + + +1. **Never** insert interpretation BEFORE the code it analyzes +2. **Never** skip re-reading after insertions +3. **Never** use ad-hoc Python for notebook manipulation (use notebook_helpers.py) +4. **Never** add emojis +5. **Never** modify existing code cells (l'enrichissement ajoute du markdown autour du code existant ; pour corriger une cellule code cassée, tracer une PR séparée — cf [anti-regression.md](../../.claude/rules/anti-regression.md)) + + + +### Quality Checklist + + + +Before completing enrichment: +- [ ] No consecutive code cells without markdown +- [ ] All notebooks have learning objectives header +- [ ] Prerequisites and duration specified +- [ ] Interpretation cells are AFTER code cells +- [ ] Git diff shows more insertions than deletions +- [ ] Professional French language (no emojis) +- [ ] Domain vocabulary is accurate + + + +### Tools Reference + + + +L'outil désigné (cf [CLAUDE.md](../../CLAUDE.md) « Catalogue agents / skills / scripts ») est la CLI multi-famille `scripts/notebook_tools/notebook_tools.py` : + + + +```bash +# Valider la structure d'un notebook (cellules, execution_count, outputs) +python scripts/notebook_tools/notebook_tools.py validate + + + +# Analyser les sorties (détecter les erreurs, les outputs vides) +python scripts/notebook_tools/notebook_tools.py analyze + + + +# Extraire le squelette (index de cellules pour le repérage avant insertion) +python scripts/notebook_tools/notebook_tools.py skeleton +``` + + + +L'utilitaire `notebook_helpers.py` reste utile pour un listing détaillé par cellule : + + + +```bash +# Lister les cellules avec leurs indices (repérage avant insertion BOTTOM-to-TOP) +python scripts/notebook_tools/notebook_helpers.py list --verbose + + + +# Vérifier l'absence de cellules code consécutives sans markdown intercalaire +grep -A1 "cell_type.*code" + + + +# Vérifier le diff final +git diff --stat +``` + + + +Catalogue complet des scripts : [scripts-reference.md](scripts-reference.md). + + + +### Documentation associée + + + +- [notebook-conventions.md](../../.claude/rules/notebook-conventions.md) — règles C.1/C.2/C.3 (stubs sans erreur volontaire, outputs commités, scope des re-exécutions). +- [procedures-recurrentes.md](procedures-recurrentes.md) — workflow d'enrichissement et pré-commit notebook (H.3). +- [subagents-reference.md](subagents-reference.md) — catalogue des sous-agents (`notebook-enricher`, `notebook-cleaner`, `notebook-designer`). +- Les journaux de session détaillés (`enrichment-log-YYYY-MM-DD.md`) sont des artefacts locaux hors dépôt ; cette doc n'en conserve que la synthèse trans-machine. + diff --git a/docs/reference/quant-prose-census.md b/docs/reference/quant-prose-census.md index 4980fb0c5f..d32a0a8646 100644 --- a/docs/reference/quant-prose-census.md +++ b/docs/reference/quant-prose-census.md @@ -99,7 +99,7 @@ Recensement chiffré des valeurs quantitatives écrites en dur dans les cellules |---|---| | `MyIA.AI.Notebooks\ML\ML.Net\ML-6-ONNX-Python.ipynb` | 32 | | `MyIA.AI.Notebooks\ML\ML.Net\ML-6-ONNX.ipynb` | 24 | -| `MyIA.AI.Notebooks\ML\DataScienceWithAgents\01-PythonForDataScience\notebooks\1.2-Manipulation_de_Donnees_avec_NumPy.ipynb` | 22 | +| `MyIA.AI.Notebooks\ML\DataScienceWithAgents\01-Python-For-Data-Science\notebooks\1.2-Manipulation_de_Donnees_avec_NumPy.ipynb` | 22 | | `MyIA.AI.Notebooks\ML\DataScienceWithAgents\02-ML-Cours\2.5-Biais-Variance-CV-ROC.ipynb` | 22 | | `MyIA.AI.Notebooks\ML\ML.Net\ML-4-Evaluation-Python.ipynb` | 20 | diff --git a/pytest.ini b/pytest.ini index e4bca997e6..9013f82641 100644 --- a/pytest.ini +++ b/pytest.ini @@ -3,7 +3,7 @@ testpaths = scripts/notebook_tools/tests scripts/lean/tests MyIA.AI.Notebooks/GameTheory/tests - MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/tests + MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/tests MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/scripts/tests scripts/tests scripts/audit/tests diff --git a/scripts/check_testpaths_coverage.py b/scripts/check_testpaths_coverage.py index ddaab30254..df8338fe77 100644 --- a/scripts/check_testpaths_coverage.py +++ b/scripts/check_testpaths_coverage.py @@ -55,7 +55,7 @@ "MyIA.AI.Notebooks/QuantConnect/scripts/tests", "MyIA.AI.Notebooks/SymbolicAI/Lean/agent_tests/tests/test_bg_tree_lock.py", "MyIA.AI.Notebooks/SymbolicAI/Lean/agent_tests/tests/test_prover_forensic_guards.py", - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/tests", + "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/tests", # scripts/quantconnect/tests : dir entier (famille 3 de #14615) — # 254 tests / 9 modules, hermétique (mesure firsthand 2026-09-05 : # 253 verts + 1 skip de donnée, yfinance absent de l'env). diff --git a/scripts/notebook_tools/pedagogy_density_baseline.json b/scripts/notebook_tools/pedagogy_density_baseline.json index 334714f1ac..c988c2bafc 100644 --- a/scripts/notebook_tools/pedagogy_density_baseline.json +++ b/scripts/notebook_tools/pedagogy_density_baseline.json @@ -268,8 +268,8 @@ "MyIA.AI.Notebooks/IIT/IIT-01-IntroToPyPhi.ipynb": 1480.467, "MyIA.AI.Notebooks/IIT/IIT-02-AdvancedTopics.ipynb": 1097.474, "MyIA.AI.Notebooks/IIT/IIT-03-CoarseGrainingMacroPhi.ipynb": 1363.75, - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb": 1240.8, - "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb": 702.875, + "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb": 1240.8, + "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb": 702.875, "MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.1-Workflow-ML.ipynb": 1004.3, "MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.2-Descente-de-gradient.ipynb": 1026.818, "MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.3-Regression-lineaire-logistique.ipynb": 909.545, diff --git a/scripts/notebook_tools/tests/test_scan_quant_classify.py b/scripts/notebook_tools/tests/test_scan_quant_classify.py index 6ddad2e495..ee0836b124 100644 --- a/scripts/notebook_tools/tests/test_scan_quant_classify.py +++ b/scripts/notebook_tools/tests/test_scan_quant_classify.py @@ -1062,8 +1062,8 @@ def _aggregate_total(results): # seule chose que le test mesurait. # 28 notebooks, chemins relatifs a CORPUS_ML_DFA _COHORT_ML_DFA = frozenset(( - "01-PythonForDataScience/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb", - "01-PythonForDataScience/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb", + "01-Python-For-Data-Science/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb", + "01-Python-For-Data-Science/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb", "02-ML-Cours/2.1-Workflow-ML.ipynb", "02-ML-Cours/2.2-Descente-de-gradient.ipynb", "02-ML-Cours/2.3-Regression-lineaire-logistique.ipynb", diff --git a/scripts/tests/baseline_nb_nav_chain.json b/scripts/tests/baseline_nb_nav_chain.json index fce19a6ff1..337ee5ff89 100644 --- a/scripts/tests/baseline_nb_nav_chain.json +++ b/scripts/tests/baseline_nb_nav_chain.json @@ -1,2017 +1,2017 @@ -{ - "findings": [ - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/GenAI/Image/examples", - "series": "MyIA.AI.Notebooks/GenAI/Image/examples" - }, - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel", - "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" - }, - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/ML/ML.Net", - "series": "MyIA.AI.Notebooks/ML/ML.Net" - }, - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/Probas/Infer", - "series": "MyIA.AI.Notebooks/Probas/Infer" - }, - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/RL", - "series": "MyIA.AI.Notebooks/RL" - }, - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Planners/02-Classical", - "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/02-Classical" - }, - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-API", - "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-API" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-02-NormalForm-Part2-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-02c-Travelers-Dilemma-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-03-Topology2x2-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-03b-Chemins-de-Swaps-Lean-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-03e-Plan-de-deformation-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-03f-Meta-Actions-Tarifees-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-04-NashEquilibrium-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-04c-NashExistence-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-05b-Lean-Minimax-Lean.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06-EvolutionTrust-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06b-Lean-RepeatedGames-Lean-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06c-RepeatedGames-FolkTheorem-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06d-Sympathie-vs-Engagement-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06f-Bounded-Agents-Lean.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06f-Bounded-Agents-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06h-Transparent-Institutions-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-07-ExtensiveForm-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-08-CombinatorialGames-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-08c-CombinatorialGames-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-09c-Stackelberg-SecurityGame-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-10-ForwardInduction-SPE-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-11-BayesianGames-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-12-ReputationGames-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-13d-Optimistic-CFR-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-14-DifferentialGames-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-15-CooperativeGames-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-15c-CooperativeGames-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-15g-AssistanceGames-2026-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16-MechanismDesign-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16b-Automated-Mechanism-Design-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16c-Extraction-de-Revenu-DSIC-IR-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16d-Echange-de-Reins-Lean-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16e-LLM-Players-Othman-Sandholm-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16f-Lean-Assignment-Lean.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17-MultiAgent-RL-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17c-Lean-Lemons-Certificat-Lean.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17d-Lean-Screening-Signaling-Lean.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17e-Bayesian-Persuasion-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17f-Market-to-Balance-Sheet-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-18-Open-Games-et-Lentilles-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-18c-Humour-Banc-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-20b-Chemin-Minimal-Temoins-Impossibilite-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-20c-Chemin-Minimal-3x2-Ordinal-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/SocialChoice/01-Arrow-Impossibility-Theorem-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory/SocialChoice" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/SocialChoice/04-Computational-Aggregation-SAT-Z3-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory/SocialChoice" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/SocialChoice/07-Committees-Core.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory/SocialChoice" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-13-Audiobook-FishAudio-S2Pro.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-14-VoiceLeading-Rendu-GenAI.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-15-MERT2-Music-Understanding.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-16-SheetSage2-Audio-To-Score.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-6-Audiobook-Pipeline.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-8-Lecture-Analytique.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-01-Introduction-FineTuning-Python_en.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/FineTuning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-02-QLoRA-Quantization-Python_en.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/FineTuning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-03-Supervised-FineTuning-SFT-Python_en.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/FineTuning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-05-ModelMerging-Routing-Python_en.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/FineTuning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/03-Aspire-Observabilite.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/04-Aspire-Streaming-Agent.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/05-Aspire-Tests-Integration.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/06-Aspire-GardeFous-Roslyn.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/08-Aspire-AsyncFFI-Dotnet.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/09-Aspire-Harness-CopilotSdk.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_01_intro_post_training.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_02_sft_baseline.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_07_rewardspy_reward_hacking.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_08_grpo_from_scratch_toy_env.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_09_rloo_from_scratch_toy_env.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_10_gae_from_scratch_toy_env.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_11a_grpo_qwen35_rlvr.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_11b_grpo_qwen_rlvr_on_verifiers.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_11c_grpo_qwen17_rlvr.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_11d_multiseed_qwen35_4x100.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_12_multistep_delayed_credit.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_13_dapo_drgrpo_corrections.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_14_neural_thermodynamic_laws.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_15_controle_interpretabilite.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_16_vericoding_formal_verification.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_17_laya_proper_rewards_toy.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/RAG-et-Memoire-Semantique/04-Tokenisation-From-Scratch.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/RAG-et-Memoire-Semantique" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/RAG-et-Memoire-Semantique/09-KernelMemory-Multimodal.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/RAG-et-Memoire-Semantique" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Créateur de mail personnalisé.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Notebook-Generated.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Notebook-Template.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Semantic-kernel-AutoInteractive.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Workbook-Template-Python.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Workbook-Template.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-01-PhiTrajectories-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-03-RobustnessDelayedGratification-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-07-ScaleFreeSignatures-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-11-CausalAgencyProfiles-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-12e-Value-of-Information-Animat-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-13b-DecroisementDynamiqueObservable-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-14-FreeEnergySurprise-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15b-SensitivityCanonicity-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15c-MetaProxyObstruction-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15d-CechObstruction-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15e-Bridge2-RecoverabilityAgency-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15f-Bridge1bis-DecoupledFamily-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15g-EmpiricalHuangExploitation-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15h-Bridge1bis-AsymmetricFamily-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15i-Bridge1bis-2DLandscape-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15j-NerveDiscriminant-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15k-RecollementMacroCells-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15l-IndependanceGenerateur-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-17-EpsilonMachine-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-21c-SAECatastrophes-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-22-LLMSubstrat-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-23-PersonaCatastrophe-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-25-InoculationRL-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-27-SymbolInvention-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-28-CollectiveAdoption-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-29-ConceptInoculation-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-31-ContrasteTroisSubstrats-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-32-StratificationCausaleLife-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-33-SoupCollisions-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-34-BancRecollementLectures-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-35d-HumorTypologyBreakdown-SAE-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-36-FLens-FactoredGeometry-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-38-SLens-SelfLocation-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-39-CompositionRegards-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-40a-TriangulationCausale-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-40b-AnalogCognitionWaves-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-41-SAE-GeometrieFeatures-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-41b-Crosscoder-Distillation-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-42-InoculationBifurcation-Pilot-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Annexe-ProxyContextuality.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Argumentation-QBFAcceptance.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Argumentation-TruthMaintenance.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Dissociation-PhatSelfReference.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Dissociation-SaillancePregnance.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Greffe4-VoteOnChain.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Greffe5-AttributionCausale.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Synthese-CrossSubstrat.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/IIT-03-CoarseGrainingMacroPhi.ipynb", - "series": "MyIA.AI.Notebooks/IIT" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/IIT-04-Le-Probleme-de-Frontiere.ipynb", - "series": "MyIA.AI.Notebooks/IIT" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/IIT-06-L-Objet-qui-a-Mordu-IIT.ipynb", - "series": "MyIA.AI.Notebooks/IIT" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2d-Detection-AnchorFree-From-Scratch.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2e-Detection-FocalLoss-From-Scratch.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2h-YOLOv5-Bench-Ultralytics.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2i-Detection-Ultralytics-Difficult-Scenes.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-00a-Ondelettes-1D-from-scratch.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-01-Denoising-SOTA.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-02-Scattering-SOTA.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-03-Synthese-Scattering-vs-ResNet.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12b-Sequential-Orchestration.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12c-Agent-Handoff.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12d-Token-Usage.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12e-Session-Persistence.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/ML.Net/ML-04b-ModelComparison-Validity-Python.ipynb", - "series": "MyIA.AI.Notebooks/ML/ML.Net" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/ML.Net/ML-05b-Series-Temporelles-Classiques-Python.ipynb", - "series": "MyIA.AI.Notebooks/ML/ML.Net" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/ML.Net/TP-prevision-ventes.ipynb", - "series": "MyIA.AI.Notebooks/ML/ML.Net" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat/Actuariat-02-Freq-Sev-Hierarchique.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat/Actuariat-03-Actuarial-Credibility.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat/Actuariat-04-Ruine-Lundberg.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-02-Dowhy-Estimand-Intervention.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-03-Dowhy-Contrefactuel-Individuel.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-04-Dowhy-Decouverte-Structure.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-05-Dowhy-Sensibilite-Confounder.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-06-Dowhy-Instrument-Faible.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-07-Quasi-Experimental.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-08-Causal-Fairness.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/DecInfer/DecInfer-02-Lean-ExpectedUtility.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/DecInfer" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/DecInfer/DecInfer-08b-Lean-Gittins.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/DecInfer" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/Infer/Infer-18-Change-Point.ipynb", - "series": "MyIA.AI.Notebooks/Probas/Infer" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/Infer/Infer-19-Survival-Analysis.ipynb", - "series": "MyIA.AI.Notebooks/Probas/Infer" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/Infer/Infer-1b-Premiers-Modeles.ipynb", - "series": "MyIA.AI.Notebooks/Probas/Infer" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/PyMC/PyMC-16-Sparse-Gaussian-Process.ipynb", - "series": "MyIA.AI.Notebooks/Probas/PyMC" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/PyMC/PyMC-18-Change-Point.ipynb", - "series": "MyIA.AI.Notebooks/Probas/PyMC" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/PyMC/PyMC-19-Survival-Analysis.ipynb", - "series": "MyIA.AI.Notebooks/Probas/PyMC" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/ML-Research-Template.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/c1330_xrp_dt_foldwise_research.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/c875_hmm_alpha_dm_research.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/hmm_alpha_research.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m11e_ensemble_research.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m12_har_rv_j_research.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m15_lstm_rv_research.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m15_lstm_rv_sc_validation.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m3_har_asymmetric_semivariance.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m4_dlinear_vol_sc_validation.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m5_hmm_regime_research.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_l1_tsmom.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_l2_dual_momentum.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_l3_trend.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_l4_decision_transformer.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_what_dl_can_predict.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-05b-FScore-Piotroski.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/Python" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-Cloud-03b-RiskParity-Composite.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/Python" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-Cloud-13-VolTargeting.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/Python" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-Dataset-Workflow.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/Python" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_asset_class_momentum.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_commodity_term_structure.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_defensive_etf_rotation.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_long_short_harvest.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_macro_factor_rotation.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_piotroski_fscore.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_puppies_of_dow.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_volatility_regime_ml.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/intraday_volume_periodicity.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_btc_ml.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_composite_ff_aw.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_composite_mom_regime.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_iv_rank_strike_clusters.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m11ef_ensemble.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m12_har_rv_j.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m12_har_rv_j_minute.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m12_hf_btc_local.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m12_hf_dm_test.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_quality_lowvol.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_risk_parity.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_grpo.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_intro.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_multi_asset.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_ppo.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_reward_shaping.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_tactical_overlay.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-00-Reward-Model-Bradley-Terry-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-00b-Biais-Dataset-Preferences-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-00d-Reward-Trainer-TRL-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-00e-DPO-TRL-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-01-PPO-RLHF-Petit-Modele-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-04-DPO-Offline-contre-GRPO-Online-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/RL-01c-Distillation-Politique-Prolog-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/RL-06e-GRPO-Depuis-Zero-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/RL-07b-Sur-Generalisation-Climbing-Game-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/RL-10-Reward-Shaping-Curriculum-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/RL-11-POMDP-Croyances-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/RL-19-Reward-Tampering-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-11b-Picross-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-1b-NQueens-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-2-GraphColoring-Statistical-Validity-Python.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-20b-SudokuBenchmark-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-5-Timetabling-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-6-Minesweeper-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-7b-Wordle-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-8-MiniZinc-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-13b-TSP-Metaheuristics-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-18b-HyperparameterTuning-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-18c-HyperparameterTuning-Rustuna-vs-Optuna.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-22-AlgorithmSelection-Python.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-27-Sparse-Index-Tracking-Walk-Forward.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/Search/App-14-ConnectFour-Adversarial-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/Search" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/Search/App-14c-ConnectFour-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/Search" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/Search/App-32-Szpiro-Pasten-2026.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/Search" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-01-StateSpace-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-02b-NetworkX-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03-Informed-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03b-PatternDatabases-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03b-PatternDatabases.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03c-LimitedDiscrepancySearch-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03c-LimitedDiscrepancySearch.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03d-WeightedAstar-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-05-GeneticAlgorithms-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-07-MCTS-And-Beyond-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-09-LinearProgramming-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-09b-SpuriousMinima.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-09c-CombinatorialDiscrepancy.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-11-Metaheuristics-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-11c-Empirical-Algorithm-Selection.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-3-Advanced-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part2-CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-6-Hybridization-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part2-CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-7-Soft-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part2-CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-9-Distributed-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part2-CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-07d-MichalewiczDixonPrice.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-12-AxisAlignment.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-13-LandscapeDebias.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-17-ParameterControl.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-17b-Empirical-Algorithm-Selection.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-20-Langage-de-Composition.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy/MGS-28-BareBonesPSO-vs-Mealpy.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy/MGS-29-GA-vs-Mealpy.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy/MGS-31-Synthese-Croisee.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-02-Factorio-Balancer-Python.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-03-MAPF-Guarantee-Audit-Python.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-10-NeuralDiving-Coloration-Python.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Sudoku/Sudoku-12b-Z3-Linq2Z3-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Sudoku" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Sudoku/Sudoku-13-SymbolicAutomata-Python.ipynb", - "series": "MyIA.AI.Notebooks/Sudoku" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Sudoku/Sudoku-18-Comparison-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Sudoku" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Sudoku/Sudoku-19-Lean-Propagation-Lean.ipynb", - "series": "MyIA.AI.Notebooks/Sudoku" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argument_Analysis_Fallacy_Rules_Symboliques.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argument_Analysis_Gouvernance_Multi_Agents.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-01-Toulmin-Model-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-01b-Schemes-Walton-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-02b-Argumentum-Cards-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-04-Dialogues-Protocolises-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-04b-Knowledge-Base-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-05b-Multi-Backend-Routing-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-05c-Formal-Richness-Matrix-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-06-JTMS-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-07b-Communication-Channels-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-07c-Orchestration-Modes-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-08d-Restitution-3-Actes-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-08e-Argument-Profile-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Obs-01-Graphes-Dates-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Obs-02-Initiation-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Obs-03-Recollement-Lectures-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Obs-04-Recollement-Strate6-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Onto-01-AIF-OWL2-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Onto-02-CrossLinks-CSV-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Onto-03-Vertus-SKOS-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-07b-Examples-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-08b-Erdos-Formal-Conjectures-Lean.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-14b-Finiteness-Lean-Companion.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-16g-Conway-Canons.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-16h-Conway-PatternTour-Native.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-16j-Conway-Hashlife-Correctness-Native.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-21c-Descente-Budget.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23b-Lean-ERC20-Native-Companion.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-24-Calibration-Native-Companion.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-27-EdgeColoring-Tutte-Companion.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-29-Hecke-Operators-Native.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-31-Euler-Navier-Stokes.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-36-Structures-Finies-MUH-Lean.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-38-Capstone-Geometry-Lean-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/07_Meal_Planner_Data_External.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/10_Witness_Generation_Automata.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/14_Optimize_MaxSAT.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/15_BitVectors_Overflow.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/16_RealArithmetic.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/18_Einsteins_Riddle.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-11-CSharp-KnowledgeGraphs.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-14-Python-Coup-Ontologique.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-16-Python-ProofCarryingOntologies.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-3b-Python-GraphOperations.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-5b-Python-LinkedData.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-6b-Python-RDFS.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-1-LogicalLearning-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-10-ActiveAutomataLearning-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-12b-PavlovDLS-Reproduction-output.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-12b-PavlovDLS-Reproduction.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-13-Discover-TPR-output.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-15-LearnedConjectures-Solver.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-1b-LogicalLearning-Lean-Native.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-2-KnowledgeBasedLearning-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-3-RelevanceLearning-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-4-InductiveLogicProgramming-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-5-InverseResolution-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-6-ModernILP-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-8-KnowledgeGraphs-ILP-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-02f-Modal-Zoo-Lean-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-07a-Extended-Frameworks-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-07b-Ranking-Probabilistic-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-09-Preferences-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-11-Causal-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-12-Grounded-Via-TweetyProject-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-04-Aspic-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-05b-Argumentation-Lean.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/GenAI/Audio/02-Advanced/02-5-Multi-Model-TTS-Gateway.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Audio/02-Advanced" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/GenAI/Audio/02-Advanced/02-6-MIDI-Generation.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Audio/02-Advanced" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/07-Aspire-SemanticFleet-MultiConnector.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_03_dpo_direct_preference.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat/Actuariat-05-Valeur-Info-Souscription.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-19-ProceduralGeneration-WFC-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-19-ProceduralGeneration-WFC.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-13-TSP-Metaheuristics.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-8-Temporal-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part2-CSP" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-09-EverestRelief.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-10-CenterBias.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-14-IslandSynergyFound.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-15-LandscapeAnalysis.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-16-AlgorithmSelection.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-18-CecBanc.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-19-MetropolisReinsertion.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-21-Representation-vs-Algorithme.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry/Geometry-01-From-Figure-To-Equation.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry/Geometry-03-Wu-Method-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry/Geometry-03b-Ritt-Decomposition-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry/Geometry-04-DD-AR-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23-ERC20-Invariant-Companion.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Planners/02-Classical/Planners-5c-Differentiel-Atteignabilite.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/02-Classical" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/03_Sudoku_Modes_Comparison.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-08-OrbitalAssembly-Certificate-Audit-Python.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-09-RCPSP-Max-Feasibility-Bounds-Python.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" - } - ], - "wrapped": [ - { - "series": "MyIA.AI.Notebooks/Complexity", - "notebooks": 13 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/00-GenAI-Environment", - "notebooks": 6 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/Audio/01-Foundation", - "notebooks": 5 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/Audio/03-Orchestration", - "notebooks": 3 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/Image/01-Foundation", - "notebooks": 6 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/Image/03-Orchestration", - "notebooks": 4 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/Image/05-History", - "notebooks": 3 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/Texte", - "notebooks": 36 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/Vibe-Coding/Claude-Code/notebooks", - "notebooks": 5 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/Video/03-Orchestration", - "notebooks": 3 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/Video/04-Applications", - "notebooks": 6 - }, - { - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks", - "notebooks": 2 - }, - { - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours", - "notebooks": 37 - }, - { - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day4-Foundations", - "notebooks": 2 - }, - { - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day6-MLE-Star", - "notebooks": 3 - }, - { - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day7-Production", - "notebooks": 2 - }, - { - "series": "MyIA.AI.Notebooks/NLP", - "notebooks": 5 - }, - { - "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/01-Foundation", - "notebooks": 6 - }, - { - "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/03-Advanced", - "notebooks": 6 - }, - { - "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/04-NeuroSymbolic", - "notebooks": 4 - }, - { - "series": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/01-Solidity-Foundation", - "notebooks": 4 - }, - { - "series": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/03-Foundry-Testing", - "notebooks": 3 - }, - { - "series": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/04-Privacy-Cryptography", - "notebooks": 3 - }, - { - "series": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/05-Alternative-Chains", - "notebooks": 5 - } - ] -} +{ + "findings": [ + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/GenAI/Image/examples", + "series": "MyIA.AI.Notebooks/GenAI/Image/examples" + }, + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel", + "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" + }, + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/ML/ML.Net", + "series": "MyIA.AI.Notebooks/ML/ML.Net" + }, + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/Probas/Infer", + "series": "MyIA.AI.Notebooks/Probas/Infer" + }, + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/RL", + "series": "MyIA.AI.Notebooks/RL" + }, + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Planners/02-Classical", + "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/02-Classical" + }, + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-API", + "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-API" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-02-NormalForm-Part2-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-02c-Travelers-Dilemma-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-03-Topology2x2-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-03b-Chemins-de-Swaps-Lean-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-03e-Plan-de-deformation-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-03f-Meta-Actions-Tarifees-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-04-NashEquilibrium-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-04c-NashExistence-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-05b-Lean-Minimax-Lean.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06-EvolutionTrust-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06b-Lean-RepeatedGames-Lean-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06c-RepeatedGames-FolkTheorem-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06d-Sympathie-vs-Engagement-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06f-Bounded-Agents-Lean.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06f-Bounded-Agents-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06h-Transparent-Institutions-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-07-ExtensiveForm-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-08-CombinatorialGames-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-08c-CombinatorialGames-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-09c-Stackelberg-SecurityGame-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-10-ForwardInduction-SPE-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-11-BayesianGames-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-12-ReputationGames-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-13d-Optimistic-CFR-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-14-DifferentialGames-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-15-CooperativeGames-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-15c-CooperativeGames-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-15g-AssistanceGames-2026-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16-MechanismDesign-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16b-Automated-Mechanism-Design-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16c-Extraction-de-Revenu-DSIC-IR-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16d-Echange-de-Reins-Lean-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16e-LLM-Players-Othman-Sandholm-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16f-Lean-Assignment-Lean.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17-MultiAgent-RL-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17c-Lean-Lemons-Certificat-Lean.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17d-Lean-Screening-Signaling-Lean.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17e-Bayesian-Persuasion-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17f-Market-to-Balance-Sheet-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-18-Open-Games-et-Lentilles-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-18c-Humour-Banc-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-20b-Chemin-Minimal-Temoins-Impossibilite-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-20c-Chemin-Minimal-3x2-Ordinal-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/SocialChoice/01-Arrow-Impossibility-Theorem-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory/SocialChoice" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/SocialChoice/04-Computational-Aggregation-SAT-Z3-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory/SocialChoice" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/SocialChoice/07-Committees-Core.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory/SocialChoice" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-13-Audiobook-FishAudio-S2Pro.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-14-VoiceLeading-Rendu-GenAI.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-15-MERT2-Music-Understanding.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-16-SheetSage2-Audio-To-Score.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-6-Audiobook-Pipeline.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-8-Lecture-Analytique.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-01-Introduction-FineTuning-Python_en.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/FineTuning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-02-QLoRA-Quantization-Python_en.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/FineTuning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-03-Supervised-FineTuning-SFT-Python_en.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/FineTuning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-05-ModelMerging-Routing-Python_en.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/FineTuning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/03-Aspire-Observabilite.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/04-Aspire-Streaming-Agent.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/05-Aspire-Tests-Integration.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/06-Aspire-GardeFous-Roslyn.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/08-Aspire-AsyncFFI-Dotnet.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/09-Aspire-Harness-CopilotSdk.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_01_intro_post_training.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_02_sft_baseline.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_07_rewardspy_reward_hacking.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_08_grpo_from_scratch_toy_env.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_09_rloo_from_scratch_toy_env.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_10_gae_from_scratch_toy_env.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_11a_grpo_qwen35_rlvr.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_11b_grpo_qwen_rlvr_on_verifiers.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_11c_grpo_qwen17_rlvr.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_11d_multiseed_qwen35_4x100.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_12_multistep_delayed_credit.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_13_dapo_drgrpo_corrections.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_14_neural_thermodynamic_laws.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_15_controle_interpretabilite.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_16_vericoding_formal_verification.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_17_laya_proper_rewards_toy.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/RAG-et-Memoire-Semantique/04-Tokenisation-From-Scratch.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/RAG-et-Memoire-Semantique" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/RAG-et-Memoire-Semantique/09-KernelMemory-Multimodal.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/RAG-et-Memoire-Semantique" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Créateur de mail personnalisé.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Notebook-Generated.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Notebook-Template.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Semantic-kernel-AutoInteractive.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Workbook-Template-Python.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Workbook-Template.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-01-PhiTrajectories-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-03-RobustnessDelayedGratification-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-07-ScaleFreeSignatures-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-11-CausalAgencyProfiles-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-12e-Value-of-Information-Animat-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-13b-DecroisementDynamiqueObservable-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-14-FreeEnergySurprise-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15b-SensitivityCanonicity-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15c-MetaProxyObstruction-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15d-CechObstruction-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15e-Bridge2-RecoverabilityAgency-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15f-Bridge1bis-DecoupledFamily-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15g-EmpiricalHuangExploitation-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15h-Bridge1bis-AsymmetricFamily-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15i-Bridge1bis-2DLandscape-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15j-NerveDiscriminant-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15k-RecollementMacroCells-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15l-IndependanceGenerateur-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-17-EpsilonMachine-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-21c-SAECatastrophes-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-22-LLMSubstrat-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-23-PersonaCatastrophe-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-25-InoculationRL-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-27-SymbolInvention-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-28-CollectiveAdoption-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-29-ConceptInoculation-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-31-ContrasteTroisSubstrats-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-32-StratificationCausaleLife-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-33-SoupCollisions-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-34-BancRecollementLectures-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-35d-HumorTypologyBreakdown-SAE-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-36-FLens-FactoredGeometry-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-38-SLens-SelfLocation-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-39-CompositionRegards-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-40a-TriangulationCausale-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-40b-AnalogCognitionWaves-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-41-SAE-GeometrieFeatures-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-41b-Crosscoder-Distillation-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-42-InoculationBifurcation-Pilot-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Annexe-ProxyContextuality.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Argumentation-QBFAcceptance.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Argumentation-TruthMaintenance.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Dissociation-PhatSelfReference.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Dissociation-SaillancePregnance.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Greffe4-VoteOnChain.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Greffe5-AttributionCausale.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Synthese-CrossSubstrat.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/IIT-03-CoarseGrainingMacroPhi.ipynb", + "series": "MyIA.AI.Notebooks/IIT" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/IIT-04-Le-Probleme-de-Frontiere.ipynb", + "series": "MyIA.AI.Notebooks/IIT" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/IIT-06-L-Objet-qui-a-Mordu-IIT.ipynb", + "series": "MyIA.AI.Notebooks/IIT" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2d-Detection-AnchorFree-From-Scratch.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2e-Detection-FocalLoss-From-Scratch.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2h-YOLOv5-Bench-Ultralytics.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2i-Detection-Ultralytics-Difficult-Scenes.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-00a-Ondelettes-1D-from-scratch.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-01-Denoising-SOTA.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-02-Scattering-SOTA.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-03-Synthese-Scattering-vs-ResNet.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12b-Sequential-Orchestration.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12c-Agent-Handoff.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12d-Token-Usage.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12e-Session-Persistence.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/ML.Net/ML-04b-ModelComparison-Validity-Python.ipynb", + "series": "MyIA.AI.Notebooks/ML/ML.Net" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/ML.Net/ML-05b-Series-Temporelles-Classiques-Python.ipynb", + "series": "MyIA.AI.Notebooks/ML/ML.Net" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/ML.Net/TP-prevision-ventes.ipynb", + "series": "MyIA.AI.Notebooks/ML/ML.Net" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat/Actuariat-02-Freq-Sev-Hierarchique.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat/Actuariat-03-Actuarial-Credibility.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat/Actuariat-04-Ruine-Lundberg.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-02-Dowhy-Estimand-Intervention.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-03-Dowhy-Contrefactuel-Individuel.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-04-Dowhy-Decouverte-Structure.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-05-Dowhy-Sensibilite-Confounder.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-06-Dowhy-Instrument-Faible.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-07-Quasi-Experimental.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-08-Causal-Fairness.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/DecInfer/DecInfer-02-Lean-ExpectedUtility.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/DecInfer" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/DecInfer/DecInfer-08b-Lean-Gittins.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/DecInfer" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/Infer/Infer-18-Change-Point.ipynb", + "series": "MyIA.AI.Notebooks/Probas/Infer" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/Infer/Infer-19-Survival-Analysis.ipynb", + "series": "MyIA.AI.Notebooks/Probas/Infer" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/Infer/Infer-1b-Premiers-Modeles.ipynb", + "series": "MyIA.AI.Notebooks/Probas/Infer" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/PyMC/PyMC-16-Sparse-Gaussian-Process.ipynb", + "series": "MyIA.AI.Notebooks/Probas/PyMC" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/PyMC/PyMC-18-Change-Point.ipynb", + "series": "MyIA.AI.Notebooks/Probas/PyMC" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/PyMC/PyMC-19-Survival-Analysis.ipynb", + "series": "MyIA.AI.Notebooks/Probas/PyMC" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/ML-Research-Template.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/c1330_xrp_dt_foldwise_research.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/c875_hmm_alpha_dm_research.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/hmm_alpha_research.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m11e_ensemble_research.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m12_har_rv_j_research.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m15_lstm_rv_research.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m15_lstm_rv_sc_validation.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m3_har_asymmetric_semivariance.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m4_dlinear_vol_sc_validation.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m5_hmm_regime_research.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_l1_tsmom.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_l2_dual_momentum.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_l3_trend.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_l4_decision_transformer.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_what_dl_can_predict.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-05b-FScore-Piotroski.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/Python" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-Cloud-03b-RiskParity-Composite.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/Python" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-Cloud-13-VolTargeting.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/Python" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-Dataset-Workflow.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/Python" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_asset_class_momentum.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_commodity_term_structure.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_defensive_etf_rotation.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_long_short_harvest.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_macro_factor_rotation.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_piotroski_fscore.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_puppies_of_dow.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_volatility_regime_ml.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/intraday_volume_periodicity.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_btc_ml.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_composite_ff_aw.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_composite_mom_regime.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_iv_rank_strike_clusters.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m11ef_ensemble.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m12_har_rv_j.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m12_har_rv_j_minute.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m12_hf_btc_local.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m12_hf_dm_test.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_quality_lowvol.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_risk_parity.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_grpo.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_intro.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_multi_asset.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_ppo.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_reward_shaping.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_tactical_overlay.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-00-Reward-Model-Bradley-Terry-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-00b-Biais-Dataset-Preferences-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-00d-Reward-Trainer-TRL-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-00e-DPO-TRL-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-01-PPO-RLHF-Petit-Modele-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-04-DPO-Offline-contre-GRPO-Online-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/RL-01c-Distillation-Politique-Prolog-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/RL-06e-GRPO-Depuis-Zero-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/RL-07b-Sur-Generalisation-Climbing-Game-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/RL-10-Reward-Shaping-Curriculum-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/RL-11-POMDP-Croyances-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/RL-19-Reward-Tampering-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-11b-Picross-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-1b-NQueens-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-2-GraphColoring-Statistical-Validity-Python.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-20b-SudokuBenchmark-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-5-Timetabling-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-6-Minesweeper-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-7b-Wordle-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-8-MiniZinc-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-13b-TSP-Metaheuristics-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-18b-HyperparameterTuning-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-18c-HyperparameterTuning-Rustuna-vs-Optuna.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-22-AlgorithmSelection-Python.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-27-Sparse-Index-Tracking-Walk-Forward.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/Search/App-14-ConnectFour-Adversarial-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/Search" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/Search/App-14c-ConnectFour-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/Search" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/Search/App-32-Szpiro-Pasten-2026.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/Search" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-01-StateSpace-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-02b-NetworkX-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03-Informed-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03b-PatternDatabases-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03b-PatternDatabases.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03c-LimitedDiscrepancySearch-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03c-LimitedDiscrepancySearch.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03d-WeightedAstar-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-05-GeneticAlgorithms-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-07-MCTS-And-Beyond-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-09-LinearProgramming-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-09b-SpuriousMinima.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-09c-CombinatorialDiscrepancy.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-11-Metaheuristics-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-11c-Empirical-Algorithm-Selection.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-3-Advanced-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part2-CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-6-Hybridization-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part2-CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-7-Soft-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part2-CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-9-Distributed-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part2-CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-07d-MichalewiczDixonPrice.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-12-AxisAlignment.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-13-LandscapeDebias.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-17-ParameterControl.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-17b-Empirical-Algorithm-Selection.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-20-Langage-de-Composition.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy/MGS-28-BareBonesPSO-vs-Mealpy.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy/MGS-29-GA-vs-Mealpy.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy/MGS-31-Synthese-Croisee.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-02-Factorio-Balancer-Python.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-03-MAPF-Guarantee-Audit-Python.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-10-NeuralDiving-Coloration-Python.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Sudoku/Sudoku-12b-Z3-Linq2Z3-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Sudoku" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Sudoku/Sudoku-13-SymbolicAutomata-Python.ipynb", + "series": "MyIA.AI.Notebooks/Sudoku" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Sudoku/Sudoku-18-Comparison-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Sudoku" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Sudoku/Sudoku-19-Lean-Propagation-Lean.ipynb", + "series": "MyIA.AI.Notebooks/Sudoku" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argument_Analysis_Fallacy_Rules_Symboliques.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argument_Analysis_Gouvernance_Multi_Agents.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-01-Toulmin-Model-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-01b-Schemes-Walton-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-02b-Argumentum-Cards-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-04-Dialogues-Protocolises-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-04b-Knowledge-Base-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-05b-Multi-Backend-Routing-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-05c-Formal-Richness-Matrix-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-06-JTMS-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-07b-Communication-Channels-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-07c-Orchestration-Modes-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-08d-Restitution-3-Actes-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-08e-Argument-Profile-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Obs-01-Graphes-Dates-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Obs-02-Initiation-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Obs-03-Recollement-Lectures-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Obs-04-Recollement-Strate6-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Onto-01-AIF-OWL2-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Onto-02-CrossLinks-CSV-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Onto-03-Vertus-SKOS-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-07b-Examples-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-08b-Erdos-Formal-Conjectures-Lean.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-14b-Finiteness-Lean-Companion.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-16g-Conway-Canons.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-16h-Conway-PatternTour-Native.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-16j-Conway-Hashlife-Correctness-Native.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-21c-Descente-Budget.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23b-Lean-ERC20-Native-Companion.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-24-Calibration-Native-Companion.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-27-EdgeColoring-Tutte-Companion.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-29-Hecke-Operators-Native.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-31-Euler-Navier-Stokes.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-36-Structures-Finies-MUH-Lean.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-38-Capstone-Geometry-Lean-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/07_Meal_Planner_Data_External.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/10_Witness_Generation_Automata.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/14_Optimize_MaxSAT.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/15_BitVectors_Overflow.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/16_RealArithmetic.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/18_Einsteins_Riddle.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-11-CSharp-KnowledgeGraphs.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-14-Python-Coup-Ontologique.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-16-Python-ProofCarryingOntologies.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-3b-Python-GraphOperations.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-5b-Python-LinkedData.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-6b-Python-RDFS.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-1-LogicalLearning-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-10-ActiveAutomataLearning-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-12b-PavlovDLS-Reproduction-output.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-12b-PavlovDLS-Reproduction.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-13-Discover-TPR-output.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-15-LearnedConjectures-Solver.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-1b-LogicalLearning-Lean-Native.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-2-KnowledgeBasedLearning-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-3-RelevanceLearning-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-4-InductiveLogicProgramming-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-5-InverseResolution-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-6-ModernILP-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-8-KnowledgeGraphs-ILP-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-02f-Modal-Zoo-Lean-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-07a-Extended-Frameworks-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-07b-Ranking-Probabilistic-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-09-Preferences-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-11-Causal-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-12-Grounded-Via-TweetyProject-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-04-Aspic-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-05b-Argumentation-Lean.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/GenAI/Audio/02-Advanced/02-5-Multi-Model-TTS-Gateway.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Audio/02-Advanced" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/GenAI/Audio/02-Advanced/02-6-MIDI-Generation.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Audio/02-Advanced" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/07-Aspire-SemanticFleet-MultiConnector.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_03_dpo_direct_preference.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat/Actuariat-05-Valeur-Info-Souscription.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-19-ProceduralGeneration-WFC-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-19-ProceduralGeneration-WFC.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-13-TSP-Metaheuristics.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-8-Temporal-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part2-CSP" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-09-EverestRelief.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-10-CenterBias.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-14-IslandSynergyFound.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-15-LandscapeAnalysis.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-16-AlgorithmSelection.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-18-CecBanc.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-19-MetropolisReinsertion.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-21-Representation-vs-Algorithme.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry/Geometry-01-From-Figure-To-Equation.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry/Geometry-03-Wu-Method-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry/Geometry-03b-Ritt-Decomposition-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry/Geometry-04-DD-AR-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23-ERC20-Invariant-Companion.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Planners/02-Classical/Planners-5c-Differentiel-Atteignabilite.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/02-Classical" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/03_Sudoku_Modes_Comparison.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-08-OrbitalAssembly-Certificate-Audit-Python.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-09-RCPSP-Max-Feasibility-Bounds-Python.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" + } + ], + "wrapped": [ + { + "series": "MyIA.AI.Notebooks/Complexity", + "notebooks": 13 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/00-GenAI-Environment", + "notebooks": 6 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/Audio/01-Foundation", + "notebooks": 5 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/Audio/03-Orchestration", + "notebooks": 3 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/Image/01-Foundation", + "notebooks": 6 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/Image/03-Orchestration", + "notebooks": 4 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/Image/05-History", + "notebooks": 3 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/Texte", + "notebooks": 36 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/Vibe-Coding/Claude-Code/notebooks", + "notebooks": 5 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/Video/03-Orchestration", + "notebooks": 3 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/Video/04-Applications", + "notebooks": 6 + }, + { + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks", + "notebooks": 2 + }, + { + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours", + "notebooks": 37 + }, + { + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day4-Foundations", + "notebooks": 2 + }, + { + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day6-MLE-Star", + "notebooks": 3 + }, + { + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day7-Production", + "notebooks": 2 + }, + { + "series": "MyIA.AI.Notebooks/NLP", + "notebooks": 5 + }, + { + "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/01-Foundation", + "notebooks": 6 + }, + { + "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/03-Advanced", + "notebooks": 6 + }, + { + "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/04-NeuroSymbolic", + "notebooks": 4 + }, + { + "series": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/01-Solidity-Foundation", + "notebooks": 4 + }, + { + "series": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/03-Foundry-Testing", + "notebooks": 3 + }, + { + "series": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/04-Privacy-Cryptography", + "notebooks": 3 + }, + { + "series": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/05-Alternative-Chains", + "notebooks": 5 + } + ] +} From c8997e11e2e594cd741da87de7c76548e0400fb9 Mon Sep 17 00:00:00 2001 From: jsboige Date: Sat, 10 Oct 2026 17:53:34 +0200 Subject: [PATCH 2/6] fix(serie,#17417): T2-01 -- preserve CRLF line endings in the 21 referent files sed sous Git Bash avait converti CRLF->LF sur chaque fichier touche, produisant des diffs fichier-entier (2017/2017 baseline nav-chain, 242/242 guide enricher). Les ratchets differentiels y voyaient 13 compteurs de prose et 4 violations HTML_404 'nouvelles' -- artefact de fins de ligne, pas de contenu. Reparation : restauration depuis origin/main + remplacement perl binaire (preserve les octets de fin de ligne), README du dossier renomme reconstruit depuis son chemin d'origine. Diffs chirurgicaux verifies (1-7 lignes/fichier). Co-Authored-By: Claude Sonnet 5.5 --- docs/reference/notebook-enricher-guide.md | 484 +-- scripts/tests/baseline_nb_nav_chain.json | 4034 ++++++++++----------- 2 files changed, 2259 insertions(+), 2259 deletions(-) diff --git a/docs/reference/notebook-enricher-guide.md b/docs/reference/notebook-enricher-guide.md index 87785a86e2..517cbaa36e 100644 --- a/docs/reference/notebook-enricher-guide.md +++ b/docs/reference/notebook-enricher-guide.md @@ -1,242 +1,242 @@ -# Guide d'enrichissement des notebooks — méthodologie & vocabulaire par domaine - - - -> **Provenance** : consolidé depuis `.claude/agent-memory/notebook-enricher/MEMORY.md` (relocalisé ici le 2026-08-10, item-7 de #9535). Ce fichier regroupe les leçons trans-machine de l'agent `notebook-enricher` : règles de positionnement des cellules, vocabulaire pédagogique par famille, patrons de contenu et checklist qualité. Il complète [.claude/rules/notebook-conventions.md](../../.claude/rules/notebook-conventions.md) (règles C.1/C.2/C.3) et [docs/reference/procedures-recurrentes.md](procedures-recurrentes.md) (workflow d'enrichissement). -> -> **Note de fraîcheur** : les « session logs » datés (2026-02 à 2026-06) sont conservés comme provenance historique ; les références de fichiers `enrichment-log-*.md` / `enrichment_summary_*.md` pointent vers des journaux de session locaux non conservés dans le dépôt. Les noms de notebooks cités peuvent avoir évolué (renumérotation #5081). - - - -## Règles de positionnement des cellules (CRITIQUE) - - - -### Cell Positioning Rules (CRITICAL) - - - -**Golden Rule**: Work BOTTOM to TOP to avoid index shifting during insertions. - - - -**Verification**: After each insertion batch, re-read the notebook to confirm cell_id references. - - - -**Never**: Insert cells before re-reading when doing multiple insertions in one notebook. - - - -### Successful Enrichment Sessions - - - -- **2026-02-15**: QC-Py-24-Autoencoders-Anomaly (11 cells added, 7.5/10 → 9.0/10) - - VAE + HMM for anomaly detection and regime switching - - BOTTOM-to-TOP insertion strategy flawless (11 cells, no errors) - - Fixed code/markdown ratio from 62/38 to 43/57 - - All interpretations positioned correctly after code outputs - - Added interpretations for: data structure, features, VAE training, anomaly detection, VAE comparison, transition matrix, HMM comparison, regime characterization, backtest, model sizes, QC production code - - Critical transition cell between VAE and HMM sections - - Summary report: `enrichment_summary_qc24.md` - - - -- **2026-02-15**: QC-Py-23-Attention-Transformers (11 cells added, 7/10 → 8.5/10) - - Advanced ML/SSM content with PyTorch implementations - - BOTTOM-to-TOP insertion strategy flawless (11 cells, no errors) - - Fixed code/markdown ratio from 62/38 to 43/57 - - All interpretations positioned correctly after code outputs - - Added interpretations for: complexity viz, SSM demo, S4 test, Mamba test, training results, SST hybrid, benchmark - - Summary report: `enrichment_summary_qc23.md` - - - -- **2026-02-07**: DataScienceWithAgents Labs (3 notebooks, 11 cells added) — journal de session local `enrichment-log-2026-02-07.md` (non conservé dans le dépôt) - - All cells positioned correctly on first attempt - - BOTTOM-to-TOP strategy prevented index conflicts - - No git rollbacks needed -- **2026-02-16**: Sudoku-10-ORTools (anciennement « Sudoku-03-ORTools » avant la renumérotation #5081 ; 4 cells added: 1 header with objectives, 2 interpretations, 1 footer) - - Navigation header and footer with Search notebook links - - Learning objectives (Bloom taxonomy) for CSP, CP-SAT, MIP - - Duration: 50 minutes, Prerequisites: Sudoku-01-Backtracking - - Interpretations after CP solver test and performance comparison - - BOTTOM-to-TOP strategy with re-read between each insertion -- **2026-02-16**: Search — notebook métaheuristiques (anciennement « Search-9-Metaheuristics » avant la renumérotation #5081 ; 3 cells added: 2 interpretations, 1 code improvement) - - Added interpretation after parameter analysis visualization (pop_size impact) - - Added interpretation after PSO convergence visualization with technical note - - Replaced seaborn with matplotlib in comparative plots (removed dependency) - - All cells positioned correctly, BOTTOM-to-TOP strategy used -- **2026-02-19**: Video GPU Notebooks Pedagogical Enhancement (6 notebooks) - - Replaced "désactivé/non disponible" messages with detailed pedagogical outputs - - Added MODE PEDAGOGIQUE sections with expected parameters, results, and reproduction code - - - Notebooks: 01-3-Qwen-VL, 01-4-ESRGAN, 02-1-HunyuanVideo, 02-2-LTX-Video, 02-3-Wan, 02-4-SVD - - Used edit_mode="replace" on existing interpretation cells (not insertions) - - No cell positioning issues since we replaced existing cells -- **2026-03-16**: GameTheory Series Header Standardization (12 notebooks reviewed, 6 fixed) - - Systematic review of all 17 GameTheory notebooks for header compliance - - Fixed pattern: "**Objectifs pedagogiques** :" (bold) → "### Objectifs d'apprentissage" (header) - - Fixed pattern: "### Objectifs pedagogiques" → "### Objectifs d'apprentissage" - - Fixed pattern: "**Prerequis**" / "**Duree estimee**" bold → proper ### headers - - Notebooks fixed: 8, 11, 12, 13, 14, 15, 16, 16b, 17 (across two sessions) - - Notebooks already correct: 1, 2, 4c, 8c, 15b, 15c, 16c - - Lean notebooks (4b, 16b): only markdown cells modified, never code/lean cells - - Also fixed wrong navigation links: notebook-11 pointing to itself instead of notebook-12 - - GameTheory domain: CFR, Stackelberg, Fictitious Play, PSRO, AlphaZero, Arrow/Sen, Sprague-Grundy - - Key rule confirmed: Lean notebooks follow same header standards as Python notebooks - - - -- **2026-06-03**: DataScienceWithAgents Exercise Stubs (8 notebooks, 17 exercise pairs added for >=3 convention, See #2161) - - Added exercise stubs (markdown context + code cell) to meet >=3 exercises per notebook - - Notebooks: Lab2-RFP (+2), Lab3-CV (+2), Lab4-DataWrangling (+1), Lab5-Viz-ML (+2), Lab6-First-Agent (+3), Lab7-Data-Analysis-Agent (+2), 1.2-NumPy (+1), 1.3-Pandas (+3) - - All stubs use `None` / `pass` pattern with `# Etape N` and `# Indice` comments - - Each exercise preceded by markdown cell with objective + indices - - No `raise NotImplementedError` / `assert False` / `1/0` used - - Path correction: NumPy/Pandas notebooks are under `01-Python-For-Data-Science/` not `Track1-LangChain/` - - - -- **2026-03-03**: Lean-11-TorchLean Pedagogical Enhancement (4 cells added, SQUELETTE → COMPLET) - - TorchLean: réseaux de neurones formellement vérifiés avec Lean 4 - - BOTTOM-to-TOP insertion strategy flawless (4 cells, no errors) - - Added sections: 3.0 (API philosophy), 4.5 (numerical errors), 5.4 (IBP visualization), 8.6 (visual synthesis) - - Rich pedagogical content: API comparison PyTorch vs TorchLean, Float32 error accumulation examples, complete IBP propagation with ASCII diagrams, ecosystem workflow visualization - - Domain vocabulary: Semantic gap, IEEE-754 rounding modes (RNE, RTZ, RTP, RTN, RNA), ULP, IBP, CROWN, LiRPA, PINNs, Lyapunov control, FloVerCoq - - README status updated from SQUELETTE to COMPLET - - Summary report: `enrichment-log-2026-03-03-lean-torchlean.md` - - - -### Domain-Specific Patterns - - - -| Domain | Key Vocabulary | Common Patterns | -|--------|----------------|-----------------| -| Data Science with Agents | Pandas, LangChain, DataFrame, agent, tools, reasoning | Progressive questions (simple->complex), data cleaning workflow, agent orchestration | -| ML | accuracy, loss, overfitting, cross-validation | Train-test split, model evaluation, interpretation tables | -| ML Advanced (SSM/Transformers) | State Space Models, Mamba, S4, selective, complexity O(n), HiPPO, discretization | Architecture comparisons, complexity analysis, training curves, parameter counts | -| ML Advanced (VAE/HMM) | VAE, reconstruction error, KL divergence, beta-VAE, HMM, Viterbi, transition matrix, regime switching | Model comparison tables, anomaly detection thresholds, regime characterization, production deployment | -| Probas | prior, posterior, likelihood, inference | Bayesian updates, factor graphs, distribution visualization | -| Sudoku/Constraint Solving | CSP, CP-SAT, MIP, DecisionBuilder, AllDifferent, propagation | Solver comparison tables, performance benchmarks, constraint modeling patterns | -| Search/Optimization | fitness, convergence, population, exploration/exploitation, metaheuristics | Parameter sensitivity analysis, algorithm comparison tables, convergence plots | -| QuantConnect | backtest, alpha model, features, signals, ObjectStore | Model training → save → deploy, feature engineering, performance metrics | -| SymbolicAI/TorchLean | Semantic gap, Float32, IEEE-754, rounding modes, IBP, CROWN, LiRPA, PINNs, Lyapunov, FloVerCoq | API philosophy, numerical error accumulation, interval propagation visualization, ecosystem diagrams | -| GameTheory | Nash equilibrium, Stackelberg, CFR, Fictitious Play, PSRO, AlphaZero, Sprague-Grundy, Nim, Arrow impossibility, Sen paradox, MARL, self-play | Convergence plots, exploitability tables, strategy evolution, P/N-position analysis | - - - -### Content Strategy Templates - - - -**Introduction Cell** (BEFORE code): -- Future tense ("nous allons...", "le code va...") -- Sets expectations -- Explains "why" before "how" - - - -**Interpretation Cell** (AFTER code): -- Past/present tense ("le resultat montre...", "on observe...") -- Tables for structured data -- > **Note technique** for important details - - - -**Transition Cell** (BETWEEN sections): -- Links concepts -- Previews next steps -- Maintains pedagogical flow - - - -### Errors to Avoid - - - -1. **Never** insert interpretation BEFORE the code it analyzes -2. **Never** skip re-reading after insertions -3. **Never** use ad-hoc Python for notebook manipulation (use notebook_helpers.py) -4. **Never** add emojis -5. **Never** modify existing code cells (l'enrichissement ajoute du markdown autour du code existant ; pour corriger une cellule code cassée, tracer une PR séparée — cf [anti-regression.md](../../.claude/rules/anti-regression.md)) - - - -### Quality Checklist - - - -Before completing enrichment: -- [ ] No consecutive code cells without markdown -- [ ] All notebooks have learning objectives header -- [ ] Prerequisites and duration specified -- [ ] Interpretation cells are AFTER code cells -- [ ] Git diff shows more insertions than deletions -- [ ] Professional French language (no emojis) -- [ ] Domain vocabulary is accurate - - - -### Tools Reference - - - -L'outil désigné (cf [CLAUDE.md](../../CLAUDE.md) « Catalogue agents / skills / scripts ») est la CLI multi-famille `scripts/notebook_tools/notebook_tools.py` : - - - -```bash -# Valider la structure d'un notebook (cellules, execution_count, outputs) -python scripts/notebook_tools/notebook_tools.py validate - - - -# Analyser les sorties (détecter les erreurs, les outputs vides) -python scripts/notebook_tools/notebook_tools.py analyze - - - -# Extraire le squelette (index de cellules pour le repérage avant insertion) -python scripts/notebook_tools/notebook_tools.py skeleton -``` - - - -L'utilitaire `notebook_helpers.py` reste utile pour un listing détaillé par cellule : - - - -```bash -# Lister les cellules avec leurs indices (repérage avant insertion BOTTOM-to-TOP) -python scripts/notebook_tools/notebook_helpers.py list --verbose - - - -# Vérifier l'absence de cellules code consécutives sans markdown intercalaire -grep -A1 "cell_type.*code" - - - -# Vérifier le diff final -git diff --stat -``` - - - -Catalogue complet des scripts : [scripts-reference.md](scripts-reference.md). - - - -### Documentation associée - - - -- [notebook-conventions.md](../../.claude/rules/notebook-conventions.md) — règles C.1/C.2/C.3 (stubs sans erreur volontaire, outputs commités, scope des re-exécutions). -- [procedures-recurrentes.md](procedures-recurrentes.md) — workflow d'enrichissement et pré-commit notebook (H.3). -- [subagents-reference.md](subagents-reference.md) — catalogue des sous-agents (`notebook-enricher`, `notebook-cleaner`, `notebook-designer`). -- Les journaux de session détaillés (`enrichment-log-YYYY-MM-DD.md`) sont des artefacts locaux hors dépôt ; cette doc n'en conserve que la synthèse trans-machine. - +# Guide d'enrichissement des notebooks — méthodologie & vocabulaire par domaine + + + +> **Provenance** : consolidé depuis `.claude/agent-memory/notebook-enricher/MEMORY.md` (relocalisé ici le 2026-08-10, item-7 de #9535). Ce fichier regroupe les leçons trans-machine de l'agent `notebook-enricher` : règles de positionnement des cellules, vocabulaire pédagogique par famille, patrons de contenu et checklist qualité. Il complète [.claude/rules/notebook-conventions.md](../../.claude/rules/notebook-conventions.md) (règles C.1/C.2/C.3) et [docs/reference/procedures-recurrentes.md](procedures-recurrentes.md) (workflow d'enrichissement). +> +> **Note de fraîcheur** : les « session logs » datés (2026-02 à 2026-06) sont conservés comme provenance historique ; les références de fichiers `enrichment-log-*.md` / `enrichment_summary_*.md` pointent vers des journaux de session locaux non conservés dans le dépôt. Les noms de notebooks cités peuvent avoir évolué (renumérotation #5081). + + + +## Règles de positionnement des cellules (CRITIQUE) + + + +### Cell Positioning Rules (CRITICAL) + + + +**Golden Rule**: Work BOTTOM to TOP to avoid index shifting during insertions. + + + +**Verification**: After each insertion batch, re-read the notebook to confirm cell_id references. + + + +**Never**: Insert cells before re-reading when doing multiple insertions in one notebook. + + + +### Successful Enrichment Sessions + + + +- **2026-02-15**: QC-Py-24-Autoencoders-Anomaly (11 cells added, 7.5/10 → 9.0/10) + - VAE + HMM for anomaly detection and regime switching + - BOTTOM-to-TOP insertion strategy flawless (11 cells, no errors) + - Fixed code/markdown ratio from 62/38 to 43/57 + - All interpretations positioned correctly after code outputs + - Added interpretations for: data structure, features, VAE training, anomaly detection, VAE comparison, transition matrix, HMM comparison, regime characterization, backtest, model sizes, QC production code + - Critical transition cell between VAE and HMM sections + - Summary report: `enrichment_summary_qc24.md` + + + +- **2026-02-15**: QC-Py-23-Attention-Transformers (11 cells added, 7/10 → 8.5/10) + - Advanced ML/SSM content with PyTorch implementations + - BOTTOM-to-TOP insertion strategy flawless (11 cells, no errors) + - Fixed code/markdown ratio from 62/38 to 43/57 + - All interpretations positioned correctly after code outputs + - Added interpretations for: complexity viz, SSM demo, S4 test, Mamba test, training results, SST hybrid, benchmark + - Summary report: `enrichment_summary_qc23.md` + + + +- **2026-02-07**: DataScienceWithAgents Labs (3 notebooks, 11 cells added) — journal de session local `enrichment-log-2026-02-07.md` (non conservé dans le dépôt) + - All cells positioned correctly on first attempt + - BOTTOM-to-TOP strategy prevented index conflicts + - No git rollbacks needed +- **2026-02-16**: Sudoku-10-ORTools (anciennement « Sudoku-03-ORTools » avant la renumérotation #5081 ; 4 cells added: 1 header with objectives, 2 interpretations, 1 footer) + - Navigation header and footer with Search notebook links + - Learning objectives (Bloom taxonomy) for CSP, CP-SAT, MIP + - Duration: 50 minutes, Prerequisites: Sudoku-01-Backtracking + - Interpretations after CP solver test and performance comparison + - BOTTOM-to-TOP strategy with re-read between each insertion +- **2026-02-16**: Search — notebook métaheuristiques (anciennement « Search-9-Metaheuristics » avant la renumérotation #5081 ; 3 cells added: 2 interpretations, 1 code improvement) + - Added interpretation after parameter analysis visualization (pop_size impact) + - Added interpretation after PSO convergence visualization with technical note + - Replaced seaborn with matplotlib in comparative plots (removed dependency) + - All cells positioned correctly, BOTTOM-to-TOP strategy used +- **2026-02-19**: Video GPU Notebooks Pedagogical Enhancement (6 notebooks) + - Replaced "désactivé/non disponible" messages with detailed pedagogical outputs + - Added MODE PEDAGOGIQUE sections with expected parameters, results, and reproduction code + + - Notebooks: 01-3-Qwen-VL, 01-4-ESRGAN, 02-1-HunyuanVideo, 02-2-LTX-Video, 02-3-Wan, 02-4-SVD + - Used edit_mode="replace" on existing interpretation cells (not insertions) + - No cell positioning issues since we replaced existing cells +- **2026-03-16**: GameTheory Series Header Standardization (12 notebooks reviewed, 6 fixed) + - Systematic review of all 17 GameTheory notebooks for header compliance + - Fixed pattern: "**Objectifs pedagogiques** :" (bold) → "### Objectifs d'apprentissage" (header) + - Fixed pattern: "### Objectifs pedagogiques" → "### Objectifs d'apprentissage" + - Fixed pattern: "**Prerequis**" / "**Duree estimee**" bold → proper ### headers + - Notebooks fixed: 8, 11, 12, 13, 14, 15, 16, 16b, 17 (across two sessions) + - Notebooks already correct: 1, 2, 4c, 8c, 15b, 15c, 16c + - Lean notebooks (4b, 16b): only markdown cells modified, never code/lean cells + - Also fixed wrong navigation links: notebook-11 pointing to itself instead of notebook-12 + - GameTheory domain: CFR, Stackelberg, Fictitious Play, PSRO, AlphaZero, Arrow/Sen, Sprague-Grundy + - Key rule confirmed: Lean notebooks follow same header standards as Python notebooks + + + +- **2026-06-03**: DataScienceWithAgents Exercise Stubs (8 notebooks, 17 exercise pairs added for >=3 convention, See #2161) + - Added exercise stubs (markdown context + code cell) to meet >=3 exercises per notebook + - Notebooks: Lab2-RFP (+2), Lab3-CV (+2), Lab4-DataWrangling (+1), Lab5-Viz-ML (+2), Lab6-First-Agent (+3), Lab7-Data-Analysis-Agent (+2), 1.2-NumPy (+1), 1.3-Pandas (+3) + - All stubs use `None` / `pass` pattern with `# Etape N` and `# Indice` comments + - Each exercise preceded by markdown cell with objective + indices + - No `raise NotImplementedError` / `assert False` / `1/0` used + - Path correction: NumPy/Pandas notebooks are under `01-Python-For-Data-Science/` not `Track1-LangChain/` + + + +- **2026-03-03**: Lean-11-TorchLean Pedagogical Enhancement (4 cells added, SQUELETTE → COMPLET) + - TorchLean: réseaux de neurones formellement vérifiés avec Lean 4 + - BOTTOM-to-TOP insertion strategy flawless (4 cells, no errors) + - Added sections: 3.0 (API philosophy), 4.5 (numerical errors), 5.4 (IBP visualization), 8.6 (visual synthesis) + - Rich pedagogical content: API comparison PyTorch vs TorchLean, Float32 error accumulation examples, complete IBP propagation with ASCII diagrams, ecosystem workflow visualization + - Domain vocabulary: Semantic gap, IEEE-754 rounding modes (RNE, RTZ, RTP, RTN, RNA), ULP, IBP, CROWN, LiRPA, PINNs, Lyapunov control, FloVerCoq + - README status updated from SQUELETTE to COMPLET + - Summary report: `enrichment-log-2026-03-03-lean-torchlean.md` + + + +### Domain-Specific Patterns + + + +| Domain | Key Vocabulary | Common Patterns | +|--------|----------------|-----------------| +| Data Science with Agents | Pandas, LangChain, DataFrame, agent, tools, reasoning | Progressive questions (simple->complex), data cleaning workflow, agent orchestration | +| ML | accuracy, loss, overfitting, cross-validation | Train-test split, model evaluation, interpretation tables | +| ML Advanced (SSM/Transformers) | State Space Models, Mamba, S4, selective, complexity O(n), HiPPO, discretization | Architecture comparisons, complexity analysis, training curves, parameter counts | +| ML Advanced (VAE/HMM) | VAE, reconstruction error, KL divergence, beta-VAE, HMM, Viterbi, transition matrix, regime switching | Model comparison tables, anomaly detection thresholds, regime characterization, production deployment | +| Probas | prior, posterior, likelihood, inference | Bayesian updates, factor graphs, distribution visualization | +| Sudoku/Constraint Solving | CSP, CP-SAT, MIP, DecisionBuilder, AllDifferent, propagation | Solver comparison tables, performance benchmarks, constraint modeling patterns | +| Search/Optimization | fitness, convergence, population, exploration/exploitation, metaheuristics | Parameter sensitivity analysis, algorithm comparison tables, convergence plots | +| QuantConnect | backtest, alpha model, features, signals, ObjectStore | Model training → save → deploy, feature engineering, performance metrics | +| SymbolicAI/TorchLean | Semantic gap, Float32, IEEE-754, rounding modes, IBP, CROWN, LiRPA, PINNs, Lyapunov, FloVerCoq | API philosophy, numerical error accumulation, interval propagation visualization, ecosystem diagrams | +| GameTheory | Nash equilibrium, Stackelberg, CFR, Fictitious Play, PSRO, AlphaZero, Sprague-Grundy, Nim, Arrow impossibility, Sen paradox, MARL, self-play | Convergence plots, exploitability tables, strategy evolution, P/N-position analysis | + + + +### Content Strategy Templates + + + +**Introduction Cell** (BEFORE code): +- Future tense ("nous allons...", "le code va...") +- Sets expectations +- Explains "why" before "how" + + + +**Interpretation Cell** (AFTER code): +- Past/present tense ("le resultat montre...", "on observe...") +- Tables for structured data +- > **Note technique** for important details + + + +**Transition Cell** (BETWEEN sections): +- Links concepts +- Previews next steps +- Maintains pedagogical flow + + + +### Errors to Avoid + + + +1. **Never** insert interpretation BEFORE the code it analyzes +2. **Never** skip re-reading after insertions +3. **Never** use ad-hoc Python for notebook manipulation (use notebook_helpers.py) +4. **Never** add emojis +5. **Never** modify existing code cells (l'enrichissement ajoute du markdown autour du code existant ; pour corriger une cellule code cassée, tracer une PR séparée — cf [anti-regression.md](../../.claude/rules/anti-regression.md)) + + + +### Quality Checklist + + + +Before completing enrichment: +- [ ] No consecutive code cells without markdown +- [ ] All notebooks have learning objectives header +- [ ] Prerequisites and duration specified +- [ ] Interpretation cells are AFTER code cells +- [ ] Git diff shows more insertions than deletions +- [ ] Professional French language (no emojis) +- [ ] Domain vocabulary is accurate + + + +### Tools Reference + + + +L'outil désigné (cf [CLAUDE.md](../../CLAUDE.md) « Catalogue agents / skills / scripts ») est la CLI multi-famille `scripts/notebook_tools/notebook_tools.py` : + + + +```bash +# Valider la structure d'un notebook (cellules, execution_count, outputs) +python scripts/notebook_tools/notebook_tools.py validate + + + +# Analyser les sorties (détecter les erreurs, les outputs vides) +python scripts/notebook_tools/notebook_tools.py analyze + + + +# Extraire le squelette (index de cellules pour le repérage avant insertion) +python scripts/notebook_tools/notebook_tools.py skeleton +``` + + + +L'utilitaire `notebook_helpers.py` reste utile pour un listing détaillé par cellule : + + + +```bash +# Lister les cellules avec leurs indices (repérage avant insertion BOTTOM-to-TOP) +python scripts/notebook_tools/notebook_helpers.py list --verbose + + + +# Vérifier l'absence de cellules code consécutives sans markdown intercalaire +grep -A1 "cell_type.*code" + + + +# Vérifier le diff final +git diff --stat +``` + + + +Catalogue complet des scripts : [scripts-reference.md](scripts-reference.md). + + + +### Documentation associée + + + +- [notebook-conventions.md](../../.claude/rules/notebook-conventions.md) — règles C.1/C.2/C.3 (stubs sans erreur volontaire, outputs commités, scope des re-exécutions). +- [procedures-recurrentes.md](procedures-recurrentes.md) — workflow d'enrichissement et pré-commit notebook (H.3). +- [subagents-reference.md](subagents-reference.md) — catalogue des sous-agents (`notebook-enricher`, `notebook-cleaner`, `notebook-designer`). +- Les journaux de session détaillés (`enrichment-log-YYYY-MM-DD.md`) sont des artefacts locaux hors dépôt ; cette doc n'en conserve que la synthèse trans-machine. + diff --git a/scripts/tests/baseline_nb_nav_chain.json b/scripts/tests/baseline_nb_nav_chain.json index 337ee5ff89..6285fce501 100644 --- a/scripts/tests/baseline_nb_nav_chain.json +++ b/scripts/tests/baseline_nb_nav_chain.json @@ -1,2017 +1,2017 @@ -{ - "findings": [ - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/GenAI/Image/examples", - "series": "MyIA.AI.Notebooks/GenAI/Image/examples" - }, - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel", - "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" - }, - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/ML/ML.Net", - "series": "MyIA.AI.Notebooks/ML/ML.Net" - }, - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/Probas/Infer", - "series": "MyIA.AI.Notebooks/Probas/Infer" - }, - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/RL", - "series": "MyIA.AI.Notebooks/RL" - }, - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Planners/02-Classical", - "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/02-Classical" - }, - { - "kind": "independent_chain", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-API", - "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-API" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-02-NormalForm-Part2-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-02c-Travelers-Dilemma-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-03-Topology2x2-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-03b-Chemins-de-Swaps-Lean-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-03e-Plan-de-deformation-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-03f-Meta-Actions-Tarifees-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-04-NashEquilibrium-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-04c-NashExistence-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-05b-Lean-Minimax-Lean.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06-EvolutionTrust-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06b-Lean-RepeatedGames-Lean-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06c-RepeatedGames-FolkTheorem-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06d-Sympathie-vs-Engagement-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06f-Bounded-Agents-Lean.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06f-Bounded-Agents-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06h-Transparent-Institutions-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-07-ExtensiveForm-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-08-CombinatorialGames-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-08c-CombinatorialGames-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-09c-Stackelberg-SecurityGame-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-10-ForwardInduction-SPE-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-11-BayesianGames-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-12-ReputationGames-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-13d-Optimistic-CFR-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-14-DifferentialGames-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-15-CooperativeGames-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-15c-CooperativeGames-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-15g-AssistanceGames-2026-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16-MechanismDesign-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16b-Automated-Mechanism-Design-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16c-Extraction-de-Revenu-DSIC-IR-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16d-Echange-de-Reins-Lean-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16e-LLM-Players-Othman-Sandholm-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16f-Lean-Assignment-Lean.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17-MultiAgent-RL-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17c-Lean-Lemons-Certificat-Lean.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17d-Lean-Screening-Signaling-Lean.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17e-Bayesian-Persuasion-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17f-Market-to-Balance-Sheet-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-18-Open-Games-et-Lentilles-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-18c-Humour-Banc-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-20b-Chemin-Minimal-Temoins-Impossibilite-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-20c-Chemin-Minimal-3x2-Ordinal-Python.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/SocialChoice/01-Arrow-Impossibility-Theorem-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory/SocialChoice" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/SocialChoice/04-Computational-Aggregation-SAT-Z3-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory/SocialChoice" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GameTheory/SocialChoice/07-Committees-Core.ipynb", - "series": "MyIA.AI.Notebooks/GameTheory/SocialChoice" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-13-Audiobook-FishAudio-S2Pro.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-14-VoiceLeading-Rendu-GenAI.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-15-MERT2-Music-Understanding.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-16-SheetSage2-Audio-To-Score.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-6-Audiobook-Pipeline.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-8-Lecture-Analytique.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-01-Introduction-FineTuning-Python_en.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/FineTuning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-02-QLoRA-Quantization-Python_en.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/FineTuning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-03-Supervised-FineTuning-SFT-Python_en.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/FineTuning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-05-ModelMerging-Routing-Python_en.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/FineTuning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/03-Aspire-Observabilite.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/04-Aspire-Streaming-Agent.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/05-Aspire-Tests-Integration.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/06-Aspire-GardeFous-Roslyn.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/08-Aspire-AsyncFFI-Dotnet.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/09-Aspire-Harness-CopilotSdk.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_01_intro_post_training.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_02_sft_baseline.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_07_rewardspy_reward_hacking.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_08_grpo_from_scratch_toy_env.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_09_rloo_from_scratch_toy_env.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_10_gae_from_scratch_toy_env.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_11a_grpo_qwen35_rlvr.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_11b_grpo_qwen_rlvr_on_verifiers.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_11c_grpo_qwen17_rlvr.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_11d_multiseed_qwen35_4x100.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_12_multistep_delayed_credit.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_13_dapo_drgrpo_corrections.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_14_neural_thermodynamic_laws.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_15_controle_interpretabilite.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_16_vericoding_formal_verification.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_17_laya_proper_rewards_toy.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/RAG-et-Memoire-Semantique/04-Tokenisation-From-Scratch.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/RAG-et-Memoire-Semantique" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/RAG-et-Memoire-Semantique/09-KernelMemory-Multimodal.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/RAG-et-Memoire-Semantique" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Créateur de mail personnalisé.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Notebook-Generated.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Notebook-Template.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Semantic-kernel-AutoInteractive.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Workbook-Template-Python.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Workbook-Template.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-01-PhiTrajectories-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-03-RobustnessDelayedGratification-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-07-ScaleFreeSignatures-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-11-CausalAgencyProfiles-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-12e-Value-of-Information-Animat-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-13b-DecroisementDynamiqueObservable-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-14-FreeEnergySurprise-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15b-SensitivityCanonicity-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15c-MetaProxyObstruction-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15d-CechObstruction-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15e-Bridge2-RecoverabilityAgency-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15f-Bridge1bis-DecoupledFamily-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15g-EmpiricalHuangExploitation-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15h-Bridge1bis-AsymmetricFamily-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15i-Bridge1bis-2DLandscape-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15j-NerveDiscriminant-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15k-RecollementMacroCells-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15l-IndependanceGenerateur-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-17-EpsilonMachine-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-21c-SAECatastrophes-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-22-LLMSubstrat-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-23-PersonaCatastrophe-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-25-InoculationRL-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-27-SymbolInvention-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-28-CollectiveAdoption-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-29-ConceptInoculation-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-31-ContrasteTroisSubstrats-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-32-StratificationCausaleLife-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-33-SoupCollisions-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-34-BancRecollementLectures-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-35d-HumorTypologyBreakdown-SAE-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-36-FLens-FactoredGeometry-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-38-SLens-SelfLocation-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-39-CompositionRegards-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-40a-TriangulationCausale-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-40b-AnalogCognitionWaves-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-41-SAE-GeometrieFeatures-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-41b-Crosscoder-Distillation-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-42-InoculationBifurcation-Pilot-Python.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Annexe-ProxyContextuality.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Argumentation-QBFAcceptance.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Argumentation-TruthMaintenance.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Dissociation-PhatSelfReference.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Dissociation-SaillancePregnance.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Greffe4-VoteOnChain.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Greffe5-AttributionCausale.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Synthese-CrossSubstrat.ipynb", - "series": "MyIA.AI.Notebooks/IIT/ICT-Series" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/IIT-03-CoarseGrainingMacroPhi.ipynb", - "series": "MyIA.AI.Notebooks/IIT" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/IIT-04-Le-Probleme-de-Frontiere.ipynb", - "series": "MyIA.AI.Notebooks/IIT" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/IIT/IIT-06-L-Objet-qui-a-Mordu-IIT.ipynb", - "series": "MyIA.AI.Notebooks/IIT" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2d-Detection-AnchorFree-From-Scratch.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2e-Detection-FocalLoss-From-Scratch.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2h-YOLOv5-Bench-Ultralytics.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2i-Detection-Ultralytics-Difficult-Scenes.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-00a-Ondelettes-1D-from-scratch.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-01-Denoising-SOTA.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-02-Scattering-SOTA.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-03-Synthese-Scattering-vs-ResNet.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12b-Sequential-Orchestration.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12c-Agent-Handoff.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12d-Token-Usage.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12e-Session-Persistence.ipynb", - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/ML.Net/ML-04b-ModelComparison-Validity-Python.ipynb", - "series": "MyIA.AI.Notebooks/ML/ML.Net" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/ML.Net/ML-05b-Series-Temporelles-Classiques-Python.ipynb", - "series": "MyIA.AI.Notebooks/ML/ML.Net" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/ML/ML.Net/TP-prevision-ventes.ipynb", - "series": "MyIA.AI.Notebooks/ML/ML.Net" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat/Actuariat-02-Freq-Sev-Hierarchique.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat/Actuariat-03-Actuarial-Credibility.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat/Actuariat-04-Ruine-Lundberg.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-02-Dowhy-Estimand-Intervention.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-03-Dowhy-Contrefactuel-Individuel.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-04-Dowhy-Decouverte-Structure.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-05-Dowhy-Sensibilite-Confounder.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-06-Dowhy-Instrument-Faible.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-07-Quasi-Experimental.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-08-Causal-Fairness.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/DecInfer/DecInfer-02-Lean-ExpectedUtility.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/DecInfer" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/DecInfer/DecInfer-08b-Lean-Gittins.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/DecInfer" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/Infer/Infer-18-Change-Point.ipynb", - "series": "MyIA.AI.Notebooks/Probas/Infer" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/Infer/Infer-19-Survival-Analysis.ipynb", - "series": "MyIA.AI.Notebooks/Probas/Infer" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/Infer/Infer-1b-Premiers-Modeles.ipynb", - "series": "MyIA.AI.Notebooks/Probas/Infer" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/PyMC/PyMC-16-Sparse-Gaussian-Process.ipynb", - "series": "MyIA.AI.Notebooks/Probas/PyMC" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/PyMC/PyMC-18-Change-Point.ipynb", - "series": "MyIA.AI.Notebooks/Probas/PyMC" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Probas/PyMC/PyMC-19-Survival-Analysis.ipynb", - "series": "MyIA.AI.Notebooks/Probas/PyMC" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/ML-Research-Template.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/c1330_xrp_dt_foldwise_research.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/c875_hmm_alpha_dm_research.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/hmm_alpha_research.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m11e_ensemble_research.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m12_har_rv_j_research.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m15_lstm_rv_research.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m15_lstm_rv_sc_validation.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m3_har_asymmetric_semivariance.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m4_dlinear_vol_sc_validation.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m5_hmm_regime_research.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_l1_tsmom.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_l2_dual_momentum.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_l3_trend.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_l4_decision_transformer.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_what_dl_can_predict.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-05b-FScore-Piotroski.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/Python" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-Cloud-03b-RiskParity-Composite.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/Python" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-Cloud-13-VolTargeting.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/Python" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-Dataset-Workflow.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/Python" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_asset_class_momentum.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_commodity_term_structure.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_defensive_etf_rotation.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_long_short_harvest.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_macro_factor_rotation.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_piotroski_fscore.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_puppies_of_dow.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_volatility_regime_ml.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/intraday_volume_periodicity.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_btc_ml.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_composite_ff_aw.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_composite_mom_regime.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_iv_rank_strike_clusters.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m11ef_ensemble.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m12_har_rv_j.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m12_har_rv_j_minute.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m12_hf_btc_local.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m12_hf_dm_test.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_quality_lowvol.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_risk_parity.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_grpo.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_intro.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_multi_asset.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_ppo.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_reward_shaping.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_tactical_overlay.ipynb", - "series": "MyIA.AI.Notebooks/QuantConnect/research" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-00-Reward-Model-Bradley-Terry-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-00b-Biais-Dataset-Preferences-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-00d-Reward-Trainer-TRL-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-00e-DPO-TRL-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-01-PPO-RLHF-Petit-Modele-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-04-DPO-Offline-contre-GRPO-Online-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL/PostTraining" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/RL-01c-Distillation-Politique-Prolog-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/RL-06e-GRPO-Depuis-Zero-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/RL-07b-Sur-Generalisation-Climbing-Game-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/RL-10-Reward-Shaping-Curriculum-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/RL-11-POMDP-Croyances-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/RL/RL-19-Reward-Tampering-Python.ipynb", - "series": "MyIA.AI.Notebooks/RL" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-11b-Picross-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-1b-NQueens-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-2-GraphColoring-Statistical-Validity-Python.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-20b-SudokuBenchmark-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-5-Timetabling-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-6-Minesweeper-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-7b-Wordle-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-8-MiniZinc-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-13b-TSP-Metaheuristics-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-18b-HyperparameterTuning-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-18c-HyperparameterTuning-Rustuna-vs-Optuna.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-22-AlgorithmSelection-Python.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-27-Sparse-Index-Tracking-Walk-Forward.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/Search/App-14-ConnectFour-Adversarial-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/Search" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/Search/App-14c-ConnectFour-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/Search" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Applications/Search/App-32-Szpiro-Pasten-2026.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/Search" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-01-StateSpace-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-02b-NetworkX-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03-Informed-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03b-PatternDatabases-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03b-PatternDatabases.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03c-LimitedDiscrepancySearch-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03c-LimitedDiscrepancySearch.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03d-WeightedAstar-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-05-GeneticAlgorithms-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-07-MCTS-And-Beyond-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-09-LinearProgramming-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-09b-SpuriousMinima.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-09c-CombinatorialDiscrepancy.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-11-Metaheuristics-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-11c-Empirical-Algorithm-Selection.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-3-Advanced-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part2-CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-6-Hybridization-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part2-CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-7-Soft-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part2-CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-9-Distributed-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part2-CSP" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-07d-MichalewiczDixonPrice.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-12-AxisAlignment.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-13-LandscapeDebias.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-17-ParameterControl.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-17b-Empirical-Algorithm-Selection.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-20-Langage-de-Composition.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy/MGS-28-BareBonesPSO-vs-Mealpy.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy/MGS-29-GA-vs-Mealpy.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy/MGS-31-Synthese-Croisee.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-02-Factorio-Balancer-Python.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-03-MAPF-Guarantee-Audit-Python.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-10-NeuralDiving-Coloration-Python.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Sudoku/Sudoku-12b-Z3-Linq2Z3-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Sudoku" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Sudoku/Sudoku-13-SymbolicAutomata-Python.ipynb", - "series": "MyIA.AI.Notebooks/Sudoku" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Sudoku/Sudoku-18-Comparison-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Sudoku" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Sudoku/Sudoku-19-Lean-Propagation-Lean.ipynb", - "series": "MyIA.AI.Notebooks/Sudoku" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argument_Analysis_Fallacy_Rules_Symboliques.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argument_Analysis_Gouvernance_Multi_Agents.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-01-Toulmin-Model-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-01b-Schemes-Walton-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-02b-Argumentum-Cards-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-04-Dialogues-Protocolises-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-04b-Knowledge-Base-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-05b-Multi-Backend-Routing-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-05c-Formal-Richness-Matrix-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-06-JTMS-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-07b-Communication-Channels-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-07c-Orchestration-Modes-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-08d-Restitution-3-Actes-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-08e-Argument-Profile-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Obs-01-Graphes-Dates-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Obs-02-Initiation-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Obs-03-Recollement-Lectures-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Obs-04-Recollement-Strate6-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Onto-01-AIF-OWL2-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Onto-02-CrossLinks-CSV-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Onto-03-Vertus-SKOS-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-07b-Examples-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-08b-Erdos-Formal-Conjectures-Lean.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-14b-Finiteness-Lean-Companion.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-16g-Conway-Canons.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-16h-Conway-PatternTour-Native.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-16j-Conway-Hashlife-Correctness-Native.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-21c-Descente-Budget.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23b-Lean-ERC20-Native-Companion.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-24-Calibration-Native-Companion.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-27-EdgeColoring-Tutte-Companion.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-29-Hecke-Operators-Native.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-31-Euler-Navier-Stokes.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-36-Structures-Finies-MUH-Lean.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-38-Capstone-Geometry-Lean-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/07_Meal_Planner_Data_External.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/10_Witness_Generation_Automata.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/14_Optimize_MaxSAT.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/15_BitVectors_Overflow.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/16_RealArithmetic.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/18_Einsteins_Riddle.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-11-CSharp-KnowledgeGraphs.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-14-Python-Coup-Ontologique.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-16-Python-ProofCarryingOntologies.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-3b-Python-GraphOperations.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-5b-Python-LinkedData.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-6b-Python-RDFS.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-1-LogicalLearning-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-10-ActiveAutomataLearning-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-12b-PavlovDLS-Reproduction-output.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-12b-PavlovDLS-Reproduction.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-13-Discover-TPR-output.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-15-LearnedConjectures-Solver.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-1b-LogicalLearning-Lean-Native.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-2-KnowledgeBasedLearning-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-3-RelevanceLearning-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-4-InductiveLogicProgramming-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-5-InverseResolution-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-6-ModernILP-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-8-KnowledgeGraphs-ILP-Csharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-02f-Modal-Zoo-Lean-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-07a-Extended-Frameworks-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-07b-Ranking-Probabilistic-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-09-Preferences-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-11-Causal-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-12-Grounded-Via-TweetyProject-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-04-Aspic-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-05b-Argumentation-Lean.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/GenAI/Audio/02-Advanced/02-5-Multi-Model-TTS-Gateway.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Audio/02-Advanced" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/GenAI/Audio/02-Advanced/02-6-MIDI-Generation.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Audio/02-Advanced" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/07-Aspire-SemanticFleet-MultiConnector.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_03_dpo_direct_preference.ipynb", - "series": "MyIA.AI.Notebooks/GenAI/PostTraining" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat/Actuariat-05-Valeur-Info-Souscription.ipynb", - "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-19-ProceduralGeneration-WFC-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-19-ProceduralGeneration-WFC.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/CSP" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-13-TSP-Metaheuristics.ipynb", - "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-8-Temporal-CSharp.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part2-CSP" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-09-EverestRelief.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-10-CenterBias.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-14-IslandSynergyFound.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-15-LandscapeAnalysis.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-16-AlgorithmSelection.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-18-CecBanc.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-19-MetropolisReinsertion.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-21-Representation-vs-Algorithme.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry/Geometry-01-From-Figure-To-Equation.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry/Geometry-03-Wu-Method-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry/Geometry-03b-Ritt-Decomposition-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry/Geometry-04-DD-AR-Python.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23-ERC20-Invariant-Companion.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/Planners/02-Classical/Planners-5c-Differentiel-Atteignabilite.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/02-Classical" - }, - { - "kind": "unreachable", - "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/03_Sudoku_Modes_Comparison.ipynb", - "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-08-OrbitalAssembly-Certificate-Audit-Python.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" - }, - { - "kind": "orphan_entry", - "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-09-RCPSP-Max-Feasibility-Bounds-Python.ipynb", - "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" - } - ], - "wrapped": [ - { - "series": "MyIA.AI.Notebooks/Complexity", - "notebooks": 13 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/00-GenAI-Environment", - "notebooks": 6 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/Audio/01-Foundation", - "notebooks": 5 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/Audio/03-Orchestration", - "notebooks": 3 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/Image/01-Foundation", - "notebooks": 6 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/Image/03-Orchestration", - "notebooks": 4 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/Image/05-History", - "notebooks": 3 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/Texte", - "notebooks": 36 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/Vibe-Coding/Claude-Code/notebooks", - "notebooks": 5 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/Video/03-Orchestration", - "notebooks": 3 - }, - { - "series": "MyIA.AI.Notebooks/GenAI/Video/04-Applications", - "notebooks": 6 - }, - { - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks", - "notebooks": 2 - }, - { - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours", - "notebooks": 37 - }, - { - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day4-Foundations", - "notebooks": 2 - }, - { - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day6-MLE-Star", - "notebooks": 3 - }, - { - "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day7-Production", - "notebooks": 2 - }, - { - "series": "MyIA.AI.Notebooks/NLP", - "notebooks": 5 - }, - { - "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/01-Foundation", - "notebooks": 6 - }, - { - "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/03-Advanced", - "notebooks": 6 - }, - { - "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/04-NeuroSymbolic", - "notebooks": 4 - }, - { - "series": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/01-Solidity-Foundation", - "notebooks": 4 - }, - { - "series": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/03-Foundry-Testing", - "notebooks": 3 - }, - { - "series": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/04-Privacy-Cryptography", - "notebooks": 3 - }, - { - "series": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/05-Alternative-Chains", - "notebooks": 5 - } - ] -} +{ + "findings": [ + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/GenAI/Image/examples", + "series": "MyIA.AI.Notebooks/GenAI/Image/examples" + }, + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel", + "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" + }, + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/ML/ML.Net", + "series": "MyIA.AI.Notebooks/ML/ML.Net" + }, + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/Probas/Infer", + "series": "MyIA.AI.Notebooks/Probas/Infer" + }, + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/RL", + "series": "MyIA.AI.Notebooks/RL" + }, + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Planners/02-Classical", + "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/02-Classical" + }, + { + "kind": "independent_chain", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-API", + "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-API" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-02-NormalForm-Part2-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-02c-Travelers-Dilemma-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-03-Topology2x2-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-03b-Chemins-de-Swaps-Lean-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-03e-Plan-de-deformation-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-03f-Meta-Actions-Tarifees-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-04-NashEquilibrium-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-04c-NashExistence-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-05b-Lean-Minimax-Lean.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06-EvolutionTrust-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06b-Lean-RepeatedGames-Lean-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06c-RepeatedGames-FolkTheorem-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06d-Sympathie-vs-Engagement-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06f-Bounded-Agents-Lean.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06f-Bounded-Agents-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-06h-Transparent-Institutions-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-07-ExtensiveForm-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-08-CombinatorialGames-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-08c-CombinatorialGames-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-09c-Stackelberg-SecurityGame-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-10-ForwardInduction-SPE-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-11-BayesianGames-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-12-ReputationGames-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-13d-Optimistic-CFR-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-14-DifferentialGames-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-15-CooperativeGames-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-15c-CooperativeGames-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-15g-AssistanceGames-2026-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16-MechanismDesign-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16b-Automated-Mechanism-Design-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16c-Extraction-de-Revenu-DSIC-IR-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16d-Echange-de-Reins-Lean-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16e-LLM-Players-Othman-Sandholm-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-16f-Lean-Assignment-Lean.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17-MultiAgent-RL-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17c-Lean-Lemons-Certificat-Lean.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17d-Lean-Screening-Signaling-Lean.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17e-Bayesian-Persuasion-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-17f-Market-to-Balance-Sheet-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-18-Open-Games-et-Lentilles-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-18c-Humour-Banc-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-20b-Chemin-Minimal-Temoins-Impossibilite-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/GameTheory-20c-Chemin-Minimal-3x2-Ordinal-Python.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/SocialChoice/01-Arrow-Impossibility-Theorem-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory/SocialChoice" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/SocialChoice/04-Computational-Aggregation-SAT-Z3-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory/SocialChoice" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GameTheory/SocialChoice/07-Committees-Core.ipynb", + "series": "MyIA.AI.Notebooks/GameTheory/SocialChoice" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-13-Audiobook-FishAudio-S2Pro.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-14-VoiceLeading-Rendu-GenAI.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-15-MERT2-Music-Understanding.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-16-SheetSage2-Audio-To-Score.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-6-Audiobook-Pipeline.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications/04-8-Lecture-Analytique.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Audio/04-Applications" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-01-Introduction-FineTuning-Python_en.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/FineTuning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-02-QLoRA-Quantization-Python_en.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/FineTuning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-03-Supervised-FineTuning-SFT-Python_en.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/FineTuning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/FineTuning/FT-05-ModelMerging-Routing-Python_en.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/FineTuning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/03-Aspire-Observabilite.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/04-Aspire-Streaming-Agent.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/05-Aspire-Tests-Integration.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/06-Aspire-GardeFous-Roslyn.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/08-Aspire-AsyncFFI-Dotnet.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/09-Aspire-Harness-CopilotSdk.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_01_intro_post_training.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_02_sft_baseline.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_07_rewardspy_reward_hacking.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_08_grpo_from_scratch_toy_env.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_09_rloo_from_scratch_toy_env.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_10_gae_from_scratch_toy_env.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_11a_grpo_qwen35_rlvr.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_11b_grpo_qwen_rlvr_on_verifiers.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_11c_grpo_qwen17_rlvr.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_11d_multiseed_qwen35_4x100.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_12_multistep_delayed_credit.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_13_dapo_drgrpo_corrections.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_14_neural_thermodynamic_laws.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_15_controle_interpretabilite.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_16_vericoding_formal_verification.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_17_laya_proper_rewards_toy.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/RAG-et-Memoire-Semantique/04-Tokenisation-From-Scratch.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/RAG-et-Memoire-Semantique" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/RAG-et-Memoire-Semantique/09-KernelMemory-Multimodal.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/RAG-et-Memoire-Semantique" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Créateur de mail personnalisé.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Notebook-Generated.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Notebook-Template.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Semantic-kernel-AutoInteractive.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Workbook-Template-Python.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/GenAI/SemanticKernel/Workbook-Template.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/SemanticKernel" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-01-PhiTrajectories-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-03-RobustnessDelayedGratification-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-07-ScaleFreeSignatures-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-11-CausalAgencyProfiles-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-12e-Value-of-Information-Animat-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-13b-DecroisementDynamiqueObservable-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-14-FreeEnergySurprise-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15b-SensitivityCanonicity-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15c-MetaProxyObstruction-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15d-CechObstruction-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15e-Bridge2-RecoverabilityAgency-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15f-Bridge1bis-DecoupledFamily-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15g-EmpiricalHuangExploitation-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15h-Bridge1bis-AsymmetricFamily-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15i-Bridge1bis-2DLandscape-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15j-NerveDiscriminant-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15k-RecollementMacroCells-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15l-IndependanceGenerateur-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-17-EpsilonMachine-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-21c-SAECatastrophes-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-22-LLMSubstrat-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-23-PersonaCatastrophe-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-25-InoculationRL-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-27-SymbolInvention-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-28-CollectiveAdoption-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-29-ConceptInoculation-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-31-ContrasteTroisSubstrats-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-32-StratificationCausaleLife-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-33-SoupCollisions-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-34-BancRecollementLectures-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-35d-HumorTypologyBreakdown-SAE-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-36-FLens-FactoredGeometry-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-38-SLens-SelfLocation-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-39-CompositionRegards-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-40a-TriangulationCausale-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-40b-AnalogCognitionWaves-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-41-SAE-GeometrieFeatures-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-41b-Crosscoder-Distillation-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-42-InoculationBifurcation-Pilot-Python.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Annexe-ProxyContextuality.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Argumentation-QBFAcceptance.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Argumentation-TruthMaintenance.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Dissociation-PhatSelfReference.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Dissociation-SaillancePregnance.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Greffe4-VoteOnChain.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Greffe5-AttributionCausale.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-Synthese-CrossSubstrat.ipynb", + "series": "MyIA.AI.Notebooks/IIT/ICT-Series" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/IIT-03-CoarseGrainingMacroPhi.ipynb", + "series": "MyIA.AI.Notebooks/IIT" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/IIT-04-Le-Probleme-de-Frontiere.ipynb", + "series": "MyIA.AI.Notebooks/IIT" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/IIT/IIT-06-L-Objet-qui-a-Mordu-IIT.ipynb", + "series": "MyIA.AI.Notebooks/IIT" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2d-Detection-AnchorFree-From-Scratch.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2e-Detection-FocalLoss-From-Scratch.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2h-YOLOv5-Bench-Ultralytics.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2i-Detection-Ultralytics-Difficult-Scenes.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-00a-Ondelettes-1D-from-scratch.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-01-Denoising-SOTA.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-02-Scattering-SOTA.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-03-Synthese-Scattering-vs-ResNet.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12b-Sequential-Orchestration.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12c-Agent-Handoff.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12d-Token-Usage.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12e-Session-Persistence.ipynb", + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/ML.Net/ML-04b-ModelComparison-Validity-Python.ipynb", + "series": "MyIA.AI.Notebooks/ML/ML.Net" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/ML.Net/ML-05b-Series-Temporelles-Classiques-Python.ipynb", + "series": "MyIA.AI.Notebooks/ML/ML.Net" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/ML/ML.Net/TP-prevision-ventes.ipynb", + "series": "MyIA.AI.Notebooks/ML/ML.Net" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat/Actuariat-02-Freq-Sev-Hierarchique.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat/Actuariat-03-Actuarial-Credibility.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat/Actuariat-04-Ruine-Lundberg.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-02-Dowhy-Estimand-Intervention.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-03-Dowhy-Contrefactuel-Individuel.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-04-Dowhy-Decouverte-Structure.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-05-Dowhy-Sensibilite-Confounder.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-06-Dowhy-Instrument-Faible.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-07-Quasi-Experimental.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges/CausalBridges-08-Causal-Fairness.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Causal-Bridges" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/DecInfer/DecInfer-02-Lean-ExpectedUtility.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/DecInfer" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/DecInfer/DecInfer-08b-Lean-Gittins.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/DecInfer" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/Infer/Infer-18-Change-Point.ipynb", + "series": "MyIA.AI.Notebooks/Probas/Infer" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/Infer/Infer-19-Survival-Analysis.ipynb", + "series": "MyIA.AI.Notebooks/Probas/Infer" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/Infer/Infer-1b-Premiers-Modeles.ipynb", + "series": "MyIA.AI.Notebooks/Probas/Infer" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/PyMC/PyMC-16-Sparse-Gaussian-Process.ipynb", + "series": "MyIA.AI.Notebooks/Probas/PyMC" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/PyMC/PyMC-18-Change-Point.ipynb", + "series": "MyIA.AI.Notebooks/Probas/PyMC" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Probas/PyMC/PyMC-19-Survival-Analysis.ipynb", + "series": "MyIA.AI.Notebooks/Probas/PyMC" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/ML-Research-Template.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/c1330_xrp_dt_foldwise_research.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/c875_hmm_alpha_dm_research.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/hmm_alpha_research.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m11e_ensemble_research.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m12_har_rv_j_research.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m15_lstm_rv_research.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m15_lstm_rv_sc_validation.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m3_har_asymmetric_semivariance.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m4_dlinear_vol_sc_validation.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/m5_hmm_regime_research.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_l1_tsmom.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_l2_dual_momentum.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_l3_trend.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_l4_decision_transformer.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline/research_what_dl_can_predict.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/ML-Training-Pipeline" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-05b-FScore-Piotroski.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/Python" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-Cloud-03b-RiskParity-Composite.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/Python" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-Cloud-13-VolTargeting.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/Python" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/Python/QC-Py-Dataset-Workflow.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/Python" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_asset_class_momentum.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_commodity_term_structure.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_defensive_etf_rotation.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_long_short_harvest.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_macro_factor_rotation.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_piotroski_fscore.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_puppies_of_dow.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor/research_volatility_regime_ml.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/projects/Research-Executor" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/intraday_volume_periodicity.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_btc_ml.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_composite_ff_aw.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_composite_mom_regime.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_iv_rank_strike_clusters.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m11ef_ensemble.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m12_har_rv_j.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m12_har_rv_j_minute.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m12_hf_btc_local.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_m12_hf_dm_test.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_quality_lowvol.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_risk_parity.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_grpo.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_intro.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_multi_asset.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_ppo.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_reward_shaping.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/QuantConnect/research/research_rl_tactical_overlay.ipynb", + "series": "MyIA.AI.Notebooks/QuantConnect/research" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-00-Reward-Model-Bradley-Terry-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-00b-Biais-Dataset-Preferences-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-00d-Reward-Trainer-TRL-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-00e-DPO-TRL-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-01-PPO-RLHF-Petit-Modele-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/PostTraining/RLPT-04-DPO-Offline-contre-GRPO-Online-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL/PostTraining" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/RL-01c-Distillation-Politique-Prolog-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/RL-06e-GRPO-Depuis-Zero-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/RL-07b-Sur-Generalisation-Climbing-Game-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/RL-10-Reward-Shaping-Curriculum-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/RL-11-POMDP-Croyances-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/RL/RL-19-Reward-Tampering-Python.ipynb", + "series": "MyIA.AI.Notebooks/RL" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-11b-Picross-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-1b-NQueens-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-2-GraphColoring-Statistical-Validity-Python.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-20b-SudokuBenchmark-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-5-Timetabling-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-6-Minesweeper-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-7b-Wordle-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-8-MiniZinc-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-13b-TSP-Metaheuristics-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-18b-HyperparameterTuning-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-18c-HyperparameterTuning-Rustuna-vs-Optuna.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-22-AlgorithmSelection-Python.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-27-Sparse-Index-Tracking-Walk-Forward.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/Search/App-14-ConnectFour-Adversarial-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/Search" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/Search/App-14c-ConnectFour-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/Search" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Applications/Search/App-32-Szpiro-Pasten-2026.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/Search" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-01-StateSpace-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-02b-NetworkX-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03-Informed-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03b-PatternDatabases-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03b-PatternDatabases.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03c-LimitedDiscrepancySearch-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03c-LimitedDiscrepancySearch.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-03d-WeightedAstar-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-05-GeneticAlgorithms-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-07-MCTS-And-Beyond-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-09-LinearProgramming-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-09b-SpuriousMinima.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-09c-CombinatorialDiscrepancy.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-11-Metaheuristics-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part1-Foundations/Search-11c-Empirical-Algorithm-Selection.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part1-Foundations" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-3-Advanced-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part2-CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-6-Hybridization-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part2-CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-7-Soft-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part2-CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-9-Distributed-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part2-CSP" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-07d-MichalewiczDixonPrice.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-12-AxisAlignment.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-13-LandscapeDebias.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-17-ParameterControl.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-17b-Empirical-Algorithm-Selection.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-20-Langage-de-Composition.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy/MGS-28-BareBonesPSO-vs-Mealpy.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy/MGS-29-GA-vs-Mealpy.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy/MGS-31-Synthese-Croisee.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-vs-mealpy" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-02-Factorio-Balancer-Python.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-03-MAPF-Guarantee-Audit-Python.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-10-NeuralDiving-Coloration-Python.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Sudoku/Sudoku-12b-Z3-Linq2Z3-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Sudoku" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Sudoku/Sudoku-13-SymbolicAutomata-Python.ipynb", + "series": "MyIA.AI.Notebooks/Sudoku" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Sudoku/Sudoku-18-Comparison-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Sudoku" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Sudoku/Sudoku-19-Lean-Propagation-Lean.ipynb", + "series": "MyIA.AI.Notebooks/Sudoku" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argument_Analysis_Fallacy_Rules_Symboliques.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argument_Analysis_Gouvernance_Multi_Agents.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-01-Toulmin-Model-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-01b-Schemes-Walton-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-02b-Argumentum-Cards-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-04-Dialogues-Protocolises-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-04b-Knowledge-Base-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-05b-Multi-Backend-Routing-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-05c-Formal-Richness-Matrix-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-06-JTMS-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-07b-Communication-Channels-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-07c-Orchestration-Modes-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-08d-Restitution-3-Actes-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-08e-Argument-Profile-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Obs-01-Graphes-Dates-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Obs-02-Initiation-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Obs-03-Recollement-Lectures-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Obs-04-Recollement-Strate6-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Onto-01-AIF-OWL2-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Onto-02-CrossLinks-CSV-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis/Argumentation-Onto-03-Vertus-SKOS-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Argument_Analysis" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-07b-Examples-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-08b-Erdos-Formal-Conjectures-Lean.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-14b-Finiteness-Lean-Companion.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-16g-Conway-Canons.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-16h-Conway-PatternTour-Native.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-16j-Conway-Hashlife-Correctness-Native.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-21c-Descente-Budget.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23b-Lean-ERC20-Native-Companion.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-24-Calibration-Native-Companion.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-27-EdgeColoring-Tutte-Companion.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-29-Hecke-Operators-Native.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-31-Euler-Navier-Stokes.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-36-Structures-Finies-MUH-Lean.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-38-Capstone-Geometry-Lean-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/07_Meal_Planner_Data_External.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/10_Witness_Generation_Automata.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/14_Optimize_MaxSAT.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/15_BitVectors_Overflow.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/16_RealArithmetic.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/18_Einsteins_Riddle.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-11-CSharp-KnowledgeGraphs.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-14-Python-Coup-Ontologique.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-16-Python-ProofCarryingOntologies.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-3b-Python-GraphOperations.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-5b-Python-LinkedData.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb/SW-6b-Python-RDFS.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SemanticWeb" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-1-LogicalLearning-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-10-ActiveAutomataLearning-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-12b-PavlovDLS-Reproduction-output.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-12b-PavlovDLS-Reproduction.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-13-Discover-TPR-output.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-15-LearnedConjectures-Solver.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-1b-LogicalLearning-Lean-Native.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-2-KnowledgeBasedLearning-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-3-RelevanceLearning-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-4-InductiveLogicProgramming-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-5-InverseResolution-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-6-ModernILP-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-8-KnowledgeGraphs-ILP-Csharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-02f-Modal-Zoo-Lean-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-07a-Extended-Frameworks-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-07b-Ranking-Probabilistic-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-09-Preferences-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-11-Causal-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-12-Grounded-Via-TweetyProject-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-04-Aspic-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Tweety/Tweety-05b-Argumentation-Lean.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Tweety" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/GenAI/Audio/02-Advanced/02-5-Multi-Model-TTS-Gateway.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Audio/02-Advanced" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/GenAI/Audio/02-Advanced/02-6-MIDI-Generation.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Audio/02-Advanced" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire/07-Aspire-SemanticFleet-MultiConnector.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/Integrations-DotNet/Aspire" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/GenAI/PostTraining/PT_03_dpo_direct_preference.ipynb", + "series": "MyIA.AI.Notebooks/GenAI/PostTraining" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat/Actuariat-05-Valeur-Info-Souscription.ipynb", + "series": "MyIA.AI.Notebooks/Probas/DecisionTheory/Actuariat" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-19-ProceduralGeneration-WFC-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Applications/CSP/App-19-ProceduralGeneration-WFC.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/CSP" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Applications/Hybrid/App-13-TSP-Metaheuristics.ipynb", + "series": "MyIA.AI.Notebooks/Search/Applications/Hybrid" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Part2-CSP/CSP-8-Temporal-CSharp.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part2-CSP" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-09-EverestRelief.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-10-CenterBias.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-14-IslandSynergyFound.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-15-LandscapeAnalysis.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-16-AlgorithmSelection.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-18-CecBanc.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-19-MetropolisReinsertion.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics/MGS-21-Representation-vs-Algorithme.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part4-Metaheuristics" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry/Geometry-01-From-Figure-To-Equation.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry/Geometry-03-Wu-Method-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry/Geometry-03b-Ritt-Decomposition-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry/Geometry-04-DD-AR-Python.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean/Geometry" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-23-ERC20-Invariant-Companion.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Lean" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/Planners/02-Classical/Planners-5c-Differentiel-Atteignabilite.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/02-Classical" + }, + { + "kind": "unreachable", + "notebook": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3/03_Sudoku_Modes_Comparison.ipynb", + "series": "MyIA.AI.Notebooks/SymbolicAI/SMT/Z3-Linq2Z3" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-08-OrbitalAssembly-Certificate-Audit-Python.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" + }, + { + "kind": "orphan_entry", + "notebook": "MyIA.AI.Notebooks/Search/Part5-Frontieres/Frontieres-09-RCPSP-Max-Feasibility-Bounds-Python.ipynb", + "series": "MyIA.AI.Notebooks/Search/Part5-Frontieres" + } + ], + "wrapped": [ + { + "series": "MyIA.AI.Notebooks/Complexity", + "notebooks": 13 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/00-GenAI-Environment", + "notebooks": 6 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/Audio/01-Foundation", + "notebooks": 5 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/Audio/03-Orchestration", + "notebooks": 3 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/Image/01-Foundation", + "notebooks": 6 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/Image/03-Orchestration", + "notebooks": 4 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/Image/05-History", + "notebooks": 3 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/Texte", + "notebooks": 36 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/Vibe-Coding/Claude-Code/notebooks", + "notebooks": 5 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/Video/03-Orchestration", + "notebooks": 3 + }, + { + "series": "MyIA.AI.Notebooks/GenAI/Video/04-Applications", + "notebooks": 6 + }, + { + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-Python-For-Data-Science/notebooks", + "notebooks": 2 + }, + { + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours", + "notebooks": 37 + }, + { + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day4-Foundations", + "notebooks": 2 + }, + { + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day6-MLE-Star", + "notebooks": 3 + }, + { + "series": "MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day7-Production", + "notebooks": 2 + }, + { + "series": "MyIA.AI.Notebooks/NLP", + "notebooks": 5 + }, + { + "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/01-Foundation", + "notebooks": 6 + }, + { + "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/03-Advanced", + "notebooks": 6 + }, + { + "series": "MyIA.AI.Notebooks/SymbolicAI/Planners/04-NeuroSymbolic", + "notebooks": 4 + }, + { + "series": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/01-Solidity-Foundation", + "notebooks": 4 + }, + { + "series": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/03-Foundry-Testing", + "notebooks": 3 + }, + { + "series": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/04-Privacy-Cryptography", + "notebooks": 3 + }, + { + "series": "MyIA.AI.Notebooks/SymbolicAI/SmartContracts/05-Alternative-Chains", + "notebooks": 5 + } + ] +} From 80aaf5a374881825d6251db81104d62cc3349296 Mon Sep 17 00:00:00 2001 From: jsboige Date: Sat, 10 Oct 2026 23:35:51 +0200 Subject: [PATCH 3/6] fix(guard,#20274): readme-link delta comparator -- pair violations re-keyed by a directory rename Un git mv de dossier re-keye chaque violation le touchant, en deux formes : le triple identique sous un nouveau chemin de README (8/12 sur T2-01), et le href change dans un README referent inchange (4/12). Le set-diff pur les comptait comme NOUVELLES+CORRIGEES symetriques (12/12, backlog inchange), rendant toute PR de renommage rouge a tort. Deux passes d'appariement, chacune prouvee sur le disque a l'etat head : - forme 1 : jumeau (class, href) exact dont le README de base a disparu ; - forme 2 : href re-key dans un README identique dont l'ancien parent resolu a disparu et le nouveau parent existe. Les controles negatifs (README/fichier de base toujours present) restent des NOUVELLES -- un copier-coller de lien casse ne se blanchit pas. Rejou local T2-01 : base=493 head=493 NOUVELLES=0 CORRIGEES=0 DEPLACEES=12. Tests : 10 passed (comparator) + 7 passed (workflow guard). Co-Authored-By: Claude Sonnet 5.5 --- .../diff_readme_link_violations.py | 88 +++++++++++++++++- .../tests/test_readme_link_violations.py | 89 +++++++++++++++++++ 2 files changed, 173 insertions(+), 4 deletions(-) diff --git a/scripts/notebook_tools/diff_readme_link_violations.py b/scripts/notebook_tools/diff_readme_link_violations.py index 7366b96c63..bfbfa6061a 100644 --- a/scripts/notebook_tools/diff_readme_link_violations.py +++ b/scripts/notebook_tools/diff_readme_link_violations.py @@ -13,6 +13,26 @@ 1 = at least one NEW violation (PR must be fixed) 2 = invocation error +Rename pairing (#20274): a `git mv` of a directory re-keys every violation +touching it, in two shapes -- the identical triple under a new README path +(violations INSIDE the renamed folder), and a changed href inside an +unchanged README (violations in referent READMEs pointing INTO the renamed +folder). Both leave the backlog count unchanged (measured on the T2-01 +rename: NOUVELLES=12, CORRIGEES=12, strictly symmetric; 8 of shape 1, +4 of shape 2). Pairing rules, each proven on disk at head state (cwd = +repo root, the fast-lane context): + + shape 1 -- a "new" (class, readme, href) whose exact (class, href) twin + sits in the fixed set AND whose base-side README no longer exists on + disk is a MOVED violation. + shape 2 -- a "new" (class, readme, href') in the SAME readme as a fixed + (class, readme, href), where the resolved old href's parent directory + has vanished and the resolved new href's parent exists: the link + tracked a folder that moved. + +The disk checks keep the pairing honest -- a re-key whose base-side file +or directory still exists is NOT a move and stays a NEW violation. + Usage: python diff_readme_link_violations.py """ @@ -20,6 +40,7 @@ import argparse import json +import posixpath import sys from pathlib import Path @@ -28,6 +49,55 @@ def _key(v: dict) -> tuple[str, str, str]: return (v.get("class", ""), v.get("readme", ""), v.get("href", "")) +def _resolve_href(readme: str, href: str) -> str: + return posixpath.normpath(posixpath.join(posixpath.dirname(readme), href)) + + +def _pair_renames( + new: list[tuple[str, str, str]], fixed: list[tuple[str, str, str]] +) -> tuple[list[tuple[str, str, str, str, str]], list[tuple[str, str, str]]]: + """Split `new` into (moved, still_new) against `fixed`. + + moved entries are (class, old_readme, old_href, new_readme, new_href); + each fixed entry pairs at most once. + """ + fixed_by_ch: dict[tuple[str, str], list[str]] = {} + fixed_by_cr: dict[tuple[str, str], list[str]] = {} + for cls, readme, href in fixed: + fixed_by_ch.setdefault((cls, href), []).append(readme) + fixed_by_cr.setdefault((cls, readme), []).append(href) + moved: list[tuple[str, str, str, str, str]] = [] + still_new: list[tuple[str, str, str]] = [] + for cls, readme, href in new: + # shape 1 -- le README lui-meme a demenage, href inchangé. + candidates = fixed_by_ch.get((cls, href), []) + source = next((r for r in candidates if not Path(r).is_file()), None) + if source is not None: + candidates.remove(source) + fixed_by_cr.get((cls, source), []).remove(href) + moved.append((cls, source, href, readme, href)) + continue + # shape 2 -- README inchangé, le href suit un dossier deplacé. + paired_href = None + for h_old in fixed_by_cr.get((cls, readme), []): + if h_old == href: + continue + p_old = _resolve_href(readme, h_old) + p_new = _resolve_href(readme, href) + if p_old == p_new: + continue + if not Path(p_old).parent.is_dir() and Path(p_new).parent.is_dir(): + paired_href = h_old + break + if paired_href is not None: + fixed_by_cr[(cls, readme)].remove(paired_href) + fixed_by_ch.get((cls, paired_href), []).remove(readme) + moved.append((cls, readme, paired_href, readme, href)) + else: + still_new.append((cls, readme, href)) + return moved, still_new + + def _load(path: Path) -> list[dict]: data = json.loads(path.read_text(encoding="utf-8")) return data.get("violations", []) @@ -51,20 +121,30 @@ def main() -> int: new = sorted(head_set - base_set) fixed = sorted(base_set - head_set) + moved, still_new = _pair_renames(new, fixed) + consumed = {(cls, old_readme, old_href) for cls, old_readme, old_href, _, _ in moved} + fixed = [t for t in fixed if t not in consumed] + print(f"README-link delta: base={len(base_set)} head={len(head_set)} " - f"NOUVELLES={len(new)} CORRIGEES={len(fixed)}") - for cls, readme, href in new: + f"NOUVELLES={len(still_new)} CORRIGEES={len(fixed)} " + f"DEPLACEES={len(moved)}") + for cls, readme, href in still_new: print(f" + {cls} {readme} -> {href}") + for cls, old_readme, old_href, new_readme, new_href in moved: + if old_href == new_href: + print(f" ~ MOVED {cls} {old_readme} => {new_readme} -> {new_href}") + else: + print(f" ~ MOVED {cls} {old_readme} {old_href} => {new_href}") for cls, readme, href in fixed: print(f" - {cls} {readme} -> {href}") - if new: + if still_new: # Meme format que le scanner (regen_quarto_render.py emet # `::error:: ...` sur stderr) pour que les outils en # aval qui grep le pattern continuent de fonctionner. La classe # est celle du dump, pas une constante : depuis #18911 c'est # HTML_404, avant c'etait STALE_LINK. - for cls, readme, href in new: + for cls, readme, href in still_new: print(f"::error::{cls} {readme} -> {href}", file=sys.stderr) return 1 return 0 diff --git a/scripts/notebook_tools/tests/test_readme_link_violations.py b/scripts/notebook_tools/tests/test_readme_link_violations.py index 0f510e8c27..466ba408a5 100644 --- a/scripts/notebook_tools/tests/test_readme_link_violations.py +++ b/scripts/notebook_tools/tests/test_readme_link_violations.py @@ -111,6 +111,95 @@ def test_diff_fails_on_injected_html_404(tmp_path: Path) -> None: assert "::error::HTML_404 WITNESS_README.md -> WITNESS.html" in proc.stderr +# --------------------------------------------------------------------------- +# #20274 -- appariement des renames (T2-01). Un `git mv` de dossier re-keye +# chaque violation : l'ancienne cle part en CORRIGEES, la nouvelle arrive en +# NOUVELLES, sans aucun changement du backlog (mesure T2-01 : 12/12). Le +# comparator apparie un couple (class, href) identique dont le README de +# base a disparu du disque (cwd = racine au state head). Les 2 temoins +# ci-dessous sont les deux cotes du discriminant. +# --------------------------------------------------------------------------- + + +def test_diff_pairs_violation_moved_by_rename(tmp_path: Path) -> None: + """Temoin positif : re-key par rename => DEPLACEES, exit 0. + + base porte la violation sur un chemin invente (absent du disque au cwd), + head la porte sur un autre chemin : couple (class, href) identique + + README de base disparu => deplacement, pas introduction. + """ + base = {"violations": [ + {"class": "HTML_404", "readme": "zz-old-dir/README.md", "href": "notebooks/1.1-X.html"} + ], "n_violations": 1} + head = {"violations": [ + {"class": "HTML_404", "readme": "zz-new-dir/README.md", "href": "notebooks/1.1-X.html"} + ], "n_violations": 1} + proc = _run_diff(base, head, tmp_path) + assert proc.returncode == 0, f"diff exit={proc.returncode}\nstdout={proc.stdout}\nstderr={proc.stderr}" + assert "NOUVELLES=0" in proc.stdout + assert "DEPLACEES=1" in proc.stdout + assert "MOVED" in proc.stdout + + +def test_diff_refuses_pairing_when_base_readme_still_exists(tmp_path: Path) -> None: + """Temoin negatif : le README de base EXISTE encore au cwd => pas d'appariement. + + Re-key d'une violation reelle (son chemin existe au REPO_ROOT par + construction) vers un chemin invente : la disparition n'est pas prouvee, + la violation re-keyee reste une NOUVELLE (exit 1). C'est ce temoin qui + empeche l'appariement de blanchir un simple copier-coller de lien casse + vers un nouveau fichier. + """ + payload = _run_dump() + real = next(v for v in payload["violations"]) + base = {"violations": [real], "n_violations": 1} + rekeyed = dict(real) + rekeyed["readme"] = "zz-invented-dir/README.md" + head = {"violations": [rekeyed], "n_violations": 1} + proc = _run_diff(base, head, tmp_path) + assert proc.returncode == 1, f"diff aurait du exit=1, exit={proc.returncode}\nstdout={proc.stdout}" + assert "NOUVELLES=1" in proc.stdout + assert "DEPLACEES=0" in proc.stdout + + +def test_diff_pairs_href_rekey_in_unchanged_readme(tmp_path: Path) -> None: + """Temoin positif passe 2 : le href suit un dossier deplace, README identique. + + Le README n'a pas bouge, le href change parce que sa cible a demenage : + l'ancien parent (invente, absent du disque au cwd) a disparu, le nouveau + parent (repertoire reel du depot) existe => deplacement, pas introduction. + """ + base = {"violations": [ + {"class": "HTML_404", "readme": "docs/README.md", "href": "../zz-gone-dir/notebooks/1.1-X.html"} + ], "n_violations": 1} + head = {"violations": [ + {"class": "HTML_404", "readme": "docs/README.md", "href": "../scripts/notebook_tools/1.1-X.html"} + ], "n_violations": 1} + proc = _run_diff(base, head, tmp_path) + assert proc.returncode == 0, f"diff exit={proc.returncode}\nstdout={proc.stdout}\nstderr={proc.stderr}" + assert "NOUVELLES=0" in proc.stdout + assert "DEPLACEES=1" in proc.stdout + + +def test_diff_refuses_href_rekey_when_old_target_dir_exists(tmp_path: Path) -> None: + """Temoin negatif passe 2 : l'ancien parent EXISTE encore => pas d'appariement. + + Un simple changement de href vers une autre cible cassee du meme dossier + existant n'est pas un demenagement : l'ancienne violation est corrigee, + la nouvelle est une introduction (exit 1). + """ + base = {"violations": [ + {"class": "HTML_404", "readme": "docs/README.md", "href": "../scripts/notebook_tools/old-X.html"} + ], "n_violations": 1} + head = {"violations": [ + {"class": "HTML_404", "readme": "docs/README.md", "href": "../scripts/notebook_tools/new-Y.html"} + ], "n_violations": 1} + proc = _run_diff(base, head, tmp_path) + assert proc.returncode == 1, f"diff aurait du exit=1, exit={proc.returncode}\nstdout={proc.stdout}" + assert "NOUVELLES=1" in proc.stdout + assert "DEPLACEES=0" in proc.stdout + + # --------------------------------------------------------------------------- # C23 / c.1443 -- propagation des pannes du scanner (adjoint po-2025). # Un defaut du wrapper transforme une indisponibilite (rc=2 / script absent From 22dff3414f7fa5015010921c1d720c7f2ed79e55 Mon Sep 17 00:00:00 2001 From: jsboige Date: Sun, 11 Oct 2026 01:33:40 +0200 Subject: [PATCH 4/6] fix(nav,#20274): baseline realigne -- ICT-15d orphan_entry->unreachable (import merge e4b3155a10) Root cause: #20100 (e4b3155a10) ajoute le renvoi Lean-25 -> ICT-15d ; ce lien entrant cross-serie retire 15d des entries du graphe nav et le reclasse unreachable (lacune externally_anchored, arbitrage #19076 ouvert). Reproduit sur origin/main pur 57f4c84c -- rouge preexistant main, invisible en CI main (workflow PR-only), importe par le merge de ce cycle. Reparation dans le precedent #19075 option b: le baseline enregistre l'etat mesure. Full check ET diff-files check: 0 NEW (verifies locaux, argv exacts CI). Le fix durable (inclure externally_anchored dans le basis du checker) reste a trancher en #19076 -- signale au coordinateur. Co-Authored-By: Claude Sonnet 5.5 --- scripts/tests/baseline_nb_nav_chain.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/tests/baseline_nb_nav_chain.json b/scripts/tests/baseline_nb_nav_chain.json index 6285fce501..6633840379 100644 --- a/scripts/tests/baseline_nb_nav_chain.json +++ b/scripts/tests/baseline_nb_nav_chain.json @@ -526,7 +526,7 @@ "series": "MyIA.AI.Notebooks/IIT/ICT-Series" }, { - "kind": "orphan_entry", + "kind": "unreachable", "notebook": "MyIA.AI.Notebooks/IIT/ICT-Series/ICT-15d-CechObstruction-Python.ipynb", "series": "MyIA.AI.Notebooks/IIT/ICT-Series" }, From 93dd488739f036710aef329527c0016d0f6005d1 Mon Sep 17 00:00:00 2001 From: jsboige Date: Sun, 11 Oct 2026 14:12:29 +0200 Subject: [PATCH 5/6] Fix(tools,#17417): pairing par mapping Git de renommages -- reserve review sur #20274 _pair_renames n'apparie plus sur absence disque seule (une suppression + une introduction unrelated passaient le meme temoin disque) : chaque appariement exige le rename enregistre par Git (git diff --find-renames base...HEAD, fail-closed : map vide = aucun MOVED). Deux temoins negatifs exiges par la reserve : suppression-sans-rename (shape 1) et cible creee sans relation (shape 2). Les temoins positifs passent par un mini-repo Git reel (git mv). Fallback HEAD^ sur push main (origin/main == HEAD). Co-Authored-By: Claude Sonnet 5.5 --- .../diff_readme_link_violations.py | 108 +++++++++-- .../tests/test_readme_link_violations.py | 172 ++++++++++++++++-- 2 files changed, 246 insertions(+), 34 deletions(-) diff --git a/scripts/notebook_tools/diff_readme_link_violations.py b/scripts/notebook_tools/diff_readme_link_violations.py index bfbfa6061a..b02c20cd1c 100644 --- a/scripts/notebook_tools/diff_readme_link_violations.py +++ b/scripts/notebook_tools/diff_readme_link_violations.py @@ -19,19 +19,29 @@ unchanged README (violations in referent READMEs pointing INTO the renamed folder). Both leave the backlog count unchanged (measured on the T2-01 rename: NOUVELLES=12, CORRIGEES=12, strictly symmetric; 8 of shape 1, -4 of shape 2). Pairing rules, each proven on disk at head state (cwd = -repo root, the fast-lane context): +4 of shape 2). + +Pairing requires a GIT rename mapping (review reserve on #20274: absence +on disk proves nothing about the relation -- only a rename recorded by +Git does). The mapping is `git diff --name-status --find-renames +--diff-filter=R $FAST_LANE_BASE...HEAD` resolved at the repo root (cwd), +the same base convention as the fast-lane engine (env `FAST_LANE_BASE`, +default `origin/main`). Fail-closed: if git is unavailable, the ref is +unknown or the diff fails, the mapping is EMPTY and no pairing happens -- +every new violation stays a NEW violation. shape 1 -- a "new" (class, readme, href) whose exact (class, href) twin - sits in the fixed set AND whose base-side README no longer exists on + sits in the fixed set AND whose base-side README maps to it by Git + rename (old readme path -> new readme path) AND no longer exists on disk is a MOVED violation. shape 2 -- a "new" (class, readme, href') in the SAME readme as a fixed - (class, readme, href), where the resolved old href's parent directory - has vanished and the resolved new href's parent exists: the link - tracked a folder that moved. + (class, readme, href), where the resolved old href maps to the resolved + new href by Git rename (old target -> new target) AND the new target's + parent directory exists: the link tracked a folder that moved. -The disk checks keep the pairing honest -- a re-key whose base-side file -or directory still exists is NOT a move and stays a NEW violation. +The Git mapping keeps the pairing honest -- a re-key whose base-side file +was deleted without a rename, or whose new target is an unrelated fresh +file, is NOT a move and stays a NEW violation. Usage: python diff_readme_link_violations.py @@ -40,7 +50,9 @@ import argparse import json +import os import posixpath +import subprocess import sys from pathlib import Path @@ -53,13 +65,69 @@ def _resolve_href(readme: str, href: str) -> str: return posixpath.normpath(posixpath.join(posixpath.dirname(readme), href)) +def _resolve_base_ref() -> str: + """Base du delta pour le mapping Git (meme convention que le fast-lane). + + FAST_LANE_BASE si posee, sinon origin/main. Sur un PUSH main + (origin/main == HEAD, le runner checkout la tete poussee), le delta + pertinent est le commit pousse : HEAD^ -- sinon le mapping serait vide + et un rename merge par push rougirait le garde a tort. + """ + ref = os.environ.get("FAST_LANE_BASE", "origin/main") + if ref == "origin/main": + try: + head = subprocess.run(["git", "rev-parse", "HEAD"], + capture_output=True, text=True, timeout=15, + encoding="utf-8", errors="replace") + origin = subprocess.run(["git", "rev-parse", "origin/main"], + capture_output=True, text=True, timeout=15, + encoding="utf-8", errors="replace") + except (subprocess.SubprocessError, OSError): + return ref + if (head.returncode == 0 and origin.returncode == 0 + and head.stdout == origin.stdout): + return "HEAD^" + return ref + + +def _git_renames(base_ref: str) -> dict[str, str]: + """Git rename mapping base...HEAD : ancien chemin -> nouveau chemin. + + Fail-closed (reserve #20274) : toute defaillance (git absent, pas de + depot, ref inconnue, timeout) rend une map VIDE -- aucun appariement + MOVED n'est alors autorise, chaque violation nouvelle reste une + NOUVELLE. L'absence sur disque ne prouve pas la relation ; seul un + rename enregistre par Git la prouve. + """ + try: + proc = subprocess.run( + ["git", "diff", "--name-status", "--find-renames", + "--diff-filter=R", f"{base_ref}...HEAD"], + capture_output=True, text=True, encoding="utf-8", + errors="replace", timeout=30, + ) + except (subprocess.SubprocessError, OSError): + return {} + if proc.returncode != 0: + return {} + renames: dict[str, str] = {} + for line in proc.stdout.splitlines(): + parts = line.split("\t") + if len(parts) == 3 and parts[0].startswith("R"): + renames[parts[1]] = parts[2] + return renames + + def _pair_renames( - new: list[tuple[str, str, str]], fixed: list[tuple[str, str, str]] + new: list[tuple[str, str, str]], fixed: list[tuple[str, str, str]], + renames: dict[str, str], ) -> tuple[list[tuple[str, str, str, str, str]], list[tuple[str, str, str]]]: """Split `new` into (moved, still_new) against `fixed`. moved entries are (class, old_readme, old_href, new_readme, new_href); - each fixed entry pairs at most once. + each fixed entry pairs at most once. Chaque appariement exige le mapping + Git de renommages (reserve #20274) : l'absence sur disque seule n'apparie + plus rien. """ fixed_by_ch: dict[tuple[str, str], list[str]] = {} fixed_by_cr: dict[tuple[str, str], list[str]] = {} @@ -69,15 +137,21 @@ def _pair_renames( moved: list[tuple[str, str, str, str, str]] = [] still_new: list[tuple[str, str, str]] = [] for cls, readme, href in new: - # shape 1 -- le README lui-meme a demenage, href inchangé. + # shape 1 -- le README lui-meme a demenage, href inchangé : le + # rename Git doit mapper l'ancien README exactement sur le nouveau. candidates = fixed_by_ch.get((cls, href), []) - source = next((r for r in candidates if not Path(r).is_file()), None) + source = next( + (r for r in candidates + if renames.get(r) == readme and not Path(r).is_file()), + None, + ) if source is not None: candidates.remove(source) fixed_by_cr.get((cls, source), []).remove(href) moved.append((cls, source, href, readme, href)) continue - # shape 2 -- README inchangé, le href suit un dossier deplacé. + # shape 2 -- README inchangé, le href suit une cible deplacee : le + # rename Git doit mapper la cible resolue ancienne sur la nouvelle. paired_href = None for h_old in fixed_by_cr.get((cls, readme), []): if h_old == href: @@ -86,7 +160,7 @@ def _pair_renames( p_new = _resolve_href(readme, href) if p_old == p_new: continue - if not Path(p_old).parent.is_dir() and Path(p_new).parent.is_dir(): + if renames.get(p_old) == p_new and Path(p_new).parent.is_dir(): paired_href = h_old break if paired_href is not None: @@ -121,13 +195,15 @@ def main() -> int: new = sorted(head_set - base_set) fixed = sorted(base_set - head_set) - moved, still_new = _pair_renames(new, fixed) + base_ref = _resolve_base_ref() + renames = _git_renames(base_ref) + moved, still_new = _pair_renames(new, fixed, renames) consumed = {(cls, old_readme, old_href) for cls, old_readme, old_href, _, _ in moved} fixed = [t for t in fixed if t not in consumed] print(f"README-link delta: base={len(base_set)} head={len(head_set)} " f"NOUVELLES={len(still_new)} CORRIGEES={len(fixed)} " - f"DEPLACEES={len(moved)}") + f"DEPLACEES={len(moved)} git-renames={len(renames)} (base {base_ref})") for cls, readme, href in still_new: print(f" + {cls} {readme} -> {href}") for cls, old_readme, old_href, new_readme, new_href in moved: diff --git a/scripts/notebook_tools/tests/test_readme_link_violations.py b/scripts/notebook_tools/tests/test_readme_link_violations.py index 466ba408a5..23a5fb3b6b 100644 --- a/scripts/notebook_tools/tests/test_readme_link_violations.py +++ b/scripts/notebook_tools/tests/test_readme_link_violations.py @@ -21,6 +21,7 @@ from __future__ import annotations import json +import os import subprocess import sys from pathlib import Path @@ -60,6 +61,75 @@ def _run_diff(base: dict, head: dict, tmp_path: Path) -> subprocess.CompletedPro ) +def _git(repo: Path, *args: str) -> None: + proc = subprocess.run( + ["git", *args], cwd=str(repo), capture_output=True, text=True, + encoding="utf-8", errors="replace", + ) + assert proc.returncode == 0, f"git {args} failed: {proc.stderr}" + + +def _make_repo( + tmp_path: Path, + moves: list[tuple[str, str]], + deletions: list[str] = (), + additions: list[str] = (), +) -> Path: + """Mini-repo Git a deux commits : base (branche main) puis tete. + + Les `moves` passent par `git mv` (renames enregistres), les `deletions` + par `git rm` (suppressions sans rename), les `additions` par creation + (fichiers sans relation). C'est la verite Git que le comparateur exige + depuis la reserve #20274 : l'absence sur disque ne prouve pas la + relation, seul le rename enregistre la prouve. + """ + repo = tmp_path / "repo" + repo.mkdir() + _git(repo, "init", "-q", "-b", "main") + _git(repo, "config", "user.name", "test") + _git(repo, "config", "user.email", "test@example.com") + for path in [old for old, _ in moves] + list(deletions): + f = repo / path + f.parent.mkdir(parents=True, exist_ok=True) + f.write_text("base\n", encoding="utf-8") + _git(repo, "add", "-A") + _git(repo, "commit", "-qm", "base") + _git(repo, "checkout", "-q", "-b", "head") + for old, new in moves: + # rename(2) exige le repertoire destination : git mv ne le cree pas. + (repo / new).parent.mkdir(parents=True, exist_ok=True) + _git(repo, "mv", old, new) + for path in deletions: + _git(repo, "rm", "-q", path) + for path in additions: + f = repo / path + f.parent.mkdir(parents=True, exist_ok=True) + f.write_text("fresh\n", encoding="utf-8") + _git(repo, "add", path) + _git(repo, "commit", "-qm", "head") + return repo + + +def _run_diff_in( + base: dict, head: dict, repo: Path, tmp_path: Path +) -> subprocess.CompletedProcess: + """_run_diff avec cwd=mini-repo et FAST_LANE_BASE=main (la base du repo).""" + base_p = tmp_path / "base.json" + head_p = tmp_path / "head.json" + base_p.write_text(json.dumps(base), encoding="utf-8") + head_p.write_text(json.dumps(head), encoding="utf-8") + env = {**os.environ, "FAST_LANE_BASE": "main"} + return subprocess.run( + [sys.executable, str(DIFF), str(base_p), str(head_p)], + cwd=str(repo), + capture_output=True, + text=True, + encoding="utf-8", + errors="replace", + env=env, + ) + + def test_json_shape_preserved() -> None: payload = _run_dump() assert isinstance(payload, dict) @@ -114,41 +184,75 @@ def test_diff_fails_on_injected_html_404(tmp_path: Path) -> None: # --------------------------------------------------------------------------- # #20274 -- appariement des renames (T2-01). Un `git mv` de dossier re-keye # chaque violation : l'ancienne cle part en CORRIGEES, la nouvelle arrive en -# NOUVELLES, sans aucun changement du backlog (mesure T2-01 : 12/12). Le -# comparator apparie un couple (class, href) identique dont le README de -# base a disparu du disque (cwd = racine au state head). Les 2 temoins -# ci-dessous sont les deux cotes du discriminant. +# NOUVELLES, sans aucun changement du backlog (mesure T2-01 : 12/12). +# Reserve de review sur #20274 : l'appariement ne se prouve plus par absence +# sur disque (une suppression + une introduction unrelated passent le meme +# temoin disque), mais par le MAPPING GIT des renommages -- seul un rename +# enregistre par Git prouve la relation. Chaque temoin construit un mini-repo +# a deux commits (base = branche main, tete = branche head) ou la verite Git +# est controlee : moves par `git mv`, temoins negatifs par suppression sans +# rename ou creation sans relation. # --------------------------------------------------------------------------- def test_diff_pairs_violation_moved_by_rename(tmp_path: Path) -> None: - """Temoin positif : re-key par rename => DEPLACEES, exit 0. + """Temoin positif : re-key par VRAI rename Git => DEPLACEES, exit 0. - base porte la violation sur un chemin invente (absent du disque au cwd), - head la porte sur un autre chemin : couple (class, href) identique + - README de base disparu => deplacement, pas introduction. + base porte la violation sur zz-old-dir/README.md, head la porte sur + zz-new-dir/README.md ; le mini-repo enregistre le rename entre les deux + par `git mv`. Couple (class, href) identique + rename Git exact => + deplacement, pas introduction. """ + repo = _make_repo(tmp_path, moves=[("zz-old-dir/README.md", "zz-new-dir/README.md")]) base = {"violations": [ {"class": "HTML_404", "readme": "zz-old-dir/README.md", "href": "notebooks/1.1-X.html"} ], "n_violations": 1} head = {"violations": [ {"class": "HTML_404", "readme": "zz-new-dir/README.md", "href": "notebooks/1.1-X.html"} ], "n_violations": 1} - proc = _run_diff(base, head, tmp_path) + proc = _run_diff_in(base, head, repo, tmp_path) assert proc.returncode == 0, f"diff exit={proc.returncode}\nstdout={proc.stdout}\nstderr={proc.stderr}" assert "NOUVELLES=0" in proc.stdout assert "DEPLACEES=1" in proc.stdout assert "MOVED" in proc.stdout +def test_diff_refuses_pairing_without_git_rename(tmp_path: Path) -> None: + """Temoin negatif (reserve #20274) : suppression + introduction unrelated. + + Le README de base est SUPPRIME (git rm) et le nouveau README est un + fichier CREE sans relation : l'ancien chemin est bien absent du disque -- + c'est exactement le faux temoin que l'absence seule blanchissait -- mais + aucun rename Git ne mappe l'ancien sur le nouveau. La violation re-keyee + reste une NOUVELLE (exit 1). + """ + repo = _make_repo( + tmp_path, + moves=[], + deletions=["zz-old-dir/README.md"], + additions=["zz-new-dir/README.md"], + ) + base = {"violations": [ + {"class": "HTML_404", "readme": "zz-old-dir/README.md", "href": "notebooks/1.1-X.html"} + ], "n_violations": 1} + head = {"violations": [ + {"class": "HTML_404", "readme": "zz-new-dir/README.md", "href": "notebooks/1.1-X.html"} + ], "n_violations": 1} + proc = _run_diff_in(base, head, repo, tmp_path) + assert proc.returncode == 1, f"diff aurait du exit=1, exit={proc.returncode}\nstdout={proc.stdout}" + assert "NOUVELLES=1" in proc.stdout + assert "DEPLACEES=0" in proc.stdout + + def test_diff_refuses_pairing_when_base_readme_still_exists(tmp_path: Path) -> None: """Temoin negatif : le README de base EXISTE encore au cwd => pas d'appariement. Re-key d'une violation reelle (son chemin existe au REPO_ROOT par - construction) vers un chemin invente : la disparition n'est pas prouvee, - la violation re-keyee reste une NOUVELLE (exit 1). C'est ce temoin qui - empeche l'appariement de blanchir un simple copier-coller de lien casse - vers un nouveau fichier. + construction) vers un chemin invente : ni le rename Git (absent du + mapping) ni la disparition sur disque ne sont prouves, la violation + re-keyee reste une NOUVELLE (exit 1). C'est ce temoin qui empeche + l'appariement de blanchir un simple copier-coller de lien casse vers un + nouveau fichier. """ payload = _run_dump() real = next(v for v in payload["violations"]) @@ -163,24 +267,56 @@ def test_diff_refuses_pairing_when_base_readme_still_exists(tmp_path: Path) -> N def test_diff_pairs_href_rekey_in_unchanged_readme(tmp_path: Path) -> None: - """Temoin positif passe 2 : le href suit un dossier deplace, README identique. + """Temoin positif passe 2 : le href suit une cible renommee par Git. Le README n'a pas bouge, le href change parce que sa cible a demenage : - l'ancien parent (invente, absent du disque au cwd) a disparu, le nouveau - parent (repertoire reel du depot) existe => deplacement, pas introduction. + le mini-repo enregistre le rename zz-gone-dir/notebooks/1.1-X.html -> + real-target/1.1-X.html par `git mv`. Rename Git sur les chemins resolus + + nouveau parent present => deplacement, pas introduction. """ + repo = _make_repo( + tmp_path, + moves=[("zz-gone-dir/notebooks/1.1-X.html", "real-target/1.1-X.html")], + ) base = {"violations": [ {"class": "HTML_404", "readme": "docs/README.md", "href": "../zz-gone-dir/notebooks/1.1-X.html"} ], "n_violations": 1} head = {"violations": [ - {"class": "HTML_404", "readme": "docs/README.md", "href": "../scripts/notebook_tools/1.1-X.html"} + {"class": "HTML_404", "readme": "docs/README.md", "href": "../real-target/1.1-X.html"} ], "n_violations": 1} - proc = _run_diff(base, head, tmp_path) + proc = _run_diff_in(base, head, repo, tmp_path) assert proc.returncode == 0, f"diff exit={proc.returncode}\nstdout={proc.stdout}\nstderr={proc.stderr}" assert "NOUVELLES=0" in proc.stdout assert "DEPLACEES=1" in proc.stdout +def test_diff_refuses_href_rekey_without_git_rename(tmp_path: Path) -> None: + """Temoin negatif (reserve #20274) : re-key de href sans relation Git. + + La cible ancienne est SUPPRIMEE (git rm) et la cible nouvelle est CREEE + independamment : l'ancien parent a bien disparu du disque -- le faux + temoin que l'absence seule blanchissait -- mais aucun rename Git ne mappe + la cible resolue ancienne sur la nouvelle. Introduction, pas deplacement + (exit 1). + """ + repo = _make_repo( + tmp_path, + moves=[], + deletions=["zz-gone-dir/1.1-X.html"], + additions=["real-target/1.1-X.html"], + ) + base = {"violations": [ + {"class": "HTML_404", "readme": "docs/README.md", "href": "../zz-gone-dir/1.1-X.html"} + ], "n_violations": 1} + head = {"violations": [ + {"class": "HTML_404", "readme": "docs/README.md", "href": "../real-target/1.1-X.html"} + ], "n_violations": 1} + proc = _run_diff_in(base, head, repo, tmp_path) + assert proc.returncode == 1, f"diff aurait du exit=1, exit={proc.returncode}\nstdout={proc.stdout}" + assert "NOUVELLES=1" in proc.stdout + assert "DEPLACEES=0" in proc.stdout + + def test_diff_refuses_href_rekey_when_old_target_dir_exists(tmp_path: Path) -> None: """Temoin negatif passe 2 : l'ancien parent EXISTE encore => pas d'appariement. From ae2b81ffd89606283e55d6da7213bc658a8e8e5b Mon Sep 17 00:00:00 2001 From: jsboige Date: Sun, 11 Oct 2026 15:57:33 +0200 Subject: [PATCH 6/6] Fix(readme,#17417): T2-01 -- prerequis 1.2 NumPy vers .ipynb reel, le dossier renomme n'a pas de rendu .html Les deux liens cross-dossier mis au nouveau nom pointaient vers notebooks/1.2-*.html : aucun .html n'est committe dans la serie (mesure : find 01-Python-For-Data-Science -name '*.html' vide ; les voisins 02-ML-Cours/*.html etaient deja 404 en base). Les deux seules NOUVELLES violations du comparateur readme-link viennent de la ; elles pointent desormais vers le .ipynb reel. Fastlane rejouee localement sur le head : 47 gardes, aucun bloquant en echec. See #20274 Co-Authored-By: Claude Sonnet 5.5 --- .../ML/DataScienceWithAgents/03-DeepLearning/README.md | 2 +- MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/README.md b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/README.md index f2cb25ae9b..c755b1087d 100644 --- a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/README.md +++ b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/README.md @@ -104,7 +104,7 @@ Cette série ancre ses carnets dans la **référence canonique du deep learning* gradient)](../02-ML-Cours/2.2-Descente-de-gradient.html), [2.8 (théorie PAC, la borne que ce notebook confronte)](../02-ML-Cours/2.8-Theorie-PAC.html) et [2.9 (le grokking boîte noire que le 3.5 rouvre à la main)](../02-ML-Cours/2.9-Grokking-Generalisation.html) -- NumPy niveau [1.2](../01-Python-For-Data-Science/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.html) +- NumPy niveau [1.2](../01-Python-For-Data-Science/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb) (produits matriciels, broadcast) ## Environnement diff --git a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/README.md b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/README.md index 7d39a264f4..70db1ae76a 100644 --- a/MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/README.md +++ b/MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/README.md @@ -46,7 +46,7 @@ Cette série suit la **même discipline que 03** : *from scratch PUIS framework, ## Prérequis - [`03-DeepLearning`](../03-DeepLearning/README.md) en entier — en particulier [3.4 (Attention-Transformer from scratch)](../03-DeepLearning/3.4-Attention-Transformer-From-Scratch.html) qui partage le mécanisme résiduel. -- NumPy niveau [`01-Python-For-Data-Science/1.2`](../01-Python-For-Data-Science/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.html) (broadcast, produits matriciels). +- NumPy niveau [`01-Python-For-Data-Science/1.2`](../01-Python-For-Data-Science/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb) (broadcast, produits matriciels). - PyTorch CPU (installé via `pip install torch torchvision`). ## Environnement