feat(tooling,#17417): script de migration DataScienceWithAgents -> ML.Python (dry-run, aucun git mv) by jsboige · Pull Request #17486 · jsboige/CoursIA

jsboige · 2026-09-23T02:32:18Z

Grain: MED/tooling -- lane myia-po-2027:CoursIA -- prev: MED/docs #17469

Quoi

Phase 2 de #17417 (dispatch ai-01, DM ai01-dispatch-17417-table-20260923) : script de migration scripts/notebook_tools/migrate_dsa_mlpython.py qui applique la table de correspondance validee (c.5786704244, amendement WS retenu) -- tranches T2 (sous-repertoires + aplanissement notebooks/), T3 (renommages N.M -> prefixe, suffixe noyau d'apres kernelspec), T5 (hub -> ML/ML.Python/).

Aucun git mv applique. Le livrable est le script + sa sortie --dry-run complete ci-dessous ; l'execution attend la chute des PRs gelantes (gate de sequenceement #5.4). Le script est fail-closed par construction :

notebook du hub non couvert par la table -> ERREUR (exit 1), aucun plan produit ;
collision de cible, cible deja trackee, kernelspec divergent de la table (re-lu a chaque run) -> ERREUR ;
--apply avec PRs gelantes encore ouvertes (--report-gating dans le meme run) -> REFUS, rien n'est execute.

Amendement de table (re-mesure, fail-closed a l'oeuvre)

Le dry-run a attrape 04-Vision/4.2i-Detection-Ultralytics-Difficult-Scenes.ipynb : merge via #16663, POSTERIEUR a la mesure de la table (origin/main @ 1536f2e4b33a, 147 chemins / 91 notebooks ; re-mesure sur 341577b1afdd : 148 chemins). Amendement mecanique au pattern T3, kernelspec python3 lu :

04-Vision/4.2i-Detection-Ultralytics-Difficult-Scenes.ipynb -> 04-Vision/Vision-02i-Detection-Ultralytics-Difficult-Scenes-Python.ipynb

(71 renommages T3 au total = 70 de la table + cet amendement, commente et date dans le code.)

Decisions de perimetre du sweep

scripts/results/ exclu : artefacts de mesure figes a leur date (results-artifact-policy) -- le chemin d'une mesure passee est un fait de mesure, pas un lien vivant.
COURSE_CATALOG.generated.* exclu : appartient a l'automation (catalog-pr-hygiene) ; il suivra le hub par regeneration a l'apply, pas par sweep manuel. Sur cette branche feature il reste byte-identique a main.
Labs des Tracks (T4) : deplacement de repertoire seulement, notebooks intacts (option 2 de la table) -- exemptes du fail-closed T3, aucun renommage individuel.
Le sweep INCLUT le hub lui-meme : les notebooks portes portent des navlinks internes relatifs (formes hub-relatives T3) qui doivent suivre.

PRs gelantes restantes (mesure au depot, 16)

#16219 (2 f.), #16376 (2), #16518 (1), #16710 (11), #16832 (2), #16858 (1), #17053 (12), #17160 (1), #17192 (14), #17382 (1), #17389 (1), #17392 (1), #17401 (1), #17410 (1), #17421 (2), #17462 (11)

La table en comptait 19 : #16663 est tombee (d'ou l'amendement ci-dessus), deux autres avec elle. Chaque gelante qui tombe peut invalider une ligne : le fail-closed re-mesure a chaque run.

Sortie --dry-run complete (mesure origin/main @ `341577b`)

=== PLAN : 148 chemins MyIA.AI.Notebooks/ML/DataScienceWithAgents -> MyIA.AI.Notebooks/ML/ML.Python ===
--- git mv (148) dont notebooks renommes T3 (71) ---
MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/README.md -> MyIA.AI.Notebooks/ML/ML.Python/01-PythonForDataScience/README.md
MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.2-Manipulation_de_Donnees_avec_NumPy.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/01-PythonForDataScience/PyDS-02-Manipulation-de-Donnees-avec-NumPy-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/notebooks/1.3-Analyse_de_Donnees_avec_Pandas.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/01-PythonForDataScience/PyDS-03-Analyse-de-Donnees-avec-Pandas-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/tests/test_numpy_basics.py -> MyIA.AI.Notebooks/ML/ML.Python/01-PythonForDataScience/tests/test_numpy_basics.py
MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/tests/test_pandas_basics.py -> MyIA.AI.Notebooks/ML/ML.Python/01-PythonForDataScience/tests/test_pandas_basics.py
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.1-Workflow-ML.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-01-Workflow-ML-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.10-Optimisation-Hyperparametres.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-10-Optimisation-Hyperparametres-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.11-Regularisation-Sparse-LASSO.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-11-Regularisation-Sparse-LASSO-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.11b-Proximal-Operators-From-Scratch.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-11b-Proximal-Operators-From-Scratch-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.11c-Lasso-SOTA-Comparison.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-11c-Lasso-SOTA-Comparison-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.11d-Optimisation-ADMM-From-Scratch.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-11d-Optimisation-ADMM-From-Scratch-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.12-Donnees-Desequilibrees.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-12-Donnees-Desequilibrees-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.13-Analyse-Erreurs.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-13-Analyse-Erreurs-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.14-Explicabilite-SHAP-LIME-Contrefactuels.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-14-Explicabilite-SHAP-LIME-Contrefactuels-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.14b-XAI-Shap-Attribution-Causal-Bridge.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-14b-XAI-Shap-Attribution-Causal-Bridge-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.2-Descente-de-gradient.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-02-Descente-de-gradient-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.3-Regression-lineaire-logistique.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-03-Regression-lineaire-logistique-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.3b-Naive-Bayes-Generatif.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-03b-Naive-Bayes-Generatif-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.3c-Regression-Grande-Dimension.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-03c-Regression-Grande-Dimension-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.3d-Modele-Gaussien-LDA-QDA.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-03d-Modele-Gaussien-LDA-QDA-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.4-Arbres-Forets-Ensembles.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-04-Arbres-Forets-Ensembles-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.5-Biais-Variance-CV-ROC.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-05-Biais-Variance-CV-ROC-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.5b-Calibration-Probabilites.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-05b-Calibration-Probabilites-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.5c-Equite-Sous-Groupes.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-05c-Equite-Sous-Groupes-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.6-Clustering-KMeans-PCA.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-06-Clustering-KMeans-PCA-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.7-Modeles-Non-Parametriques.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-07-Modeles-Non-Parametriques-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.7b-SMO-From-Scratch.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-07b-SMO-From-Scratch-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.7c-SVM-SOTA-Comparison.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-07c-SVM-SOTA-Comparison-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.8-Theorie-PAC.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-08-Theorie-PAC-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.8b-Theorie-PAC-Lean.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-08b-Theorie-PAC-Lean-Lean.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.8c-Borne-Temoin-Concentration.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-08c-Borne-Temoin-Concentration-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.8d-Lean-Novikoff-Convergence.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-08d-Lean-Novikoff-Convergence-Lean.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.9-Grokking-Generalisation.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-09-Grokking-Generalisation-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.9b-GenEFT-Theorie-Effective.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-09b-GenEFT-Theorie-Effective-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.9d-Features-Circulaires-Helice-Nombres.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-09d-Features-Circulaires-Helice-Nombres-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.9e-MIPS-Extraction-Programme.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/MLPy-09e-MIPS-Extraction-Programme-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/README.md -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/README.md
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/assets/equite-frontiere-compromis.png -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/assets/equite-frontiere-compromis.png
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/assets/equite-poche-erreurs.png -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/assets/equite-poche-erreurs.png
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/assets/readme/MANIFEST.md -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/assets/readme/MANIFEST.md
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/assets/readme/ml21-overfitting.png -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/assets/readme/ml21-overfitting.png
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/assets/readme/ml22-learning-rate.png -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/assets/readme/ml22-learning-rate.png
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/assets/readme/ml23-sigmoid.png -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/assets/readme/ml23-sigmoid.png
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/assets/readme/ml24-frontiere.png -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/assets/readme/ml24-frontiere.png
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/assets/readme/ml25-roc.png -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/assets/readme/ml25-roc.png
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/assets/readme/ml26-pca.png -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/assets/readme/ml26-pca.png
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/assets/readme/ml27b-smo-frontiere.png -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/assets/readme/ml27b-smo-frontiere.png
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/data/german_credit.csv -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/data/german_credit.csv
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/shap_kernel_marginal.png -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/shap_kernel_marginal.png
MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/shap_tree_conditional.png -> MyIA.AI.Notebooks/ML/ML.Python/02-ML-Cours/shap_tree_conditional.png
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.0-Theorie-Information.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-00-Theorie-Information-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.1-Retropropagation.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-01-Retropropagation-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.10-Modeles-Generatifs-Diffusion-SOTA.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-10-Modeles-Generatifs-Diffusion-SOTA-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.2-Optimisateurs.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-02-Optimisateurs-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.3-Regularisation.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-03-Regularisation-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.4-Attention-Transformer-From-Scratch.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-04-Attention-Transformer-From-Scratch-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.4c-MoE-from-scratch.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-04c-MoE-from-scratch-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.5-Phenomenes-de-Generalisation.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-05-Phenomenes-de-Generalisation-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.6-Modeles-Generatifs.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-06-Modeles-Generatifs-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.6b-Modeles-Generatifs-PyTorch.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-06b-Modeles-Generatifs-PyTorch-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.6c-Modeles-Generatifs-Diffusion-from-scratch.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-06c-Modeles-Generatifs-Diffusion-from-scratch-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.6d-Modeles-Generatifs-Score-SDE-from-scratch.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-06d-Modeles-Generatifs-Score-SDE-from-scratch-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.6e-Modeles-Generatifs-Conditionnels-from-scratch.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-06e-Modeles-Generatifs-Conditionnels-from-scratch-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.7-Distillation-Maitre-Eleve.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-07-Distillation-Maitre-Eleve-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.8-Representations-Contrastives.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-08-Representations-Contrastives-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.9-Compression-Quantization-FP.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-09-Compression-Quantization-FP-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.9a-Compression-Quantization-INT8.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-09a-Compression-Quantization-INT8-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.9b-Compression-Pruning-from-scratch.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-09b-Compression-Pruning-from-scratch-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.9c-Pruning-From-Scratch.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-09c-Pruning-From-Scratch-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.9e-Compression-Quantization-SOTA.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-09e-Compression-Quantization-SOTA-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.9f-Compression-Pruning-SOTA.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/DL-09f-Compression-Pruning-SOTA-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/README.md -> MyIA.AI.Notebooks/ML/ML.Python/03-DeepLearning/README.md
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.1-Conv-NumPy-Torch-Allclose.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/04-Vision/Vision-01-Conv-NumPy-Torch-Allclose-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2-ConvNet-Profonde-Residuelles.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/04-Vision/Vision-02-ConvNet-Profonde-Residuelles-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2b-Lean-GradientFlow-Vanishing.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/04-Vision/Vision-02b-Lean-GradientFlow-Vanishing-Lean.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2c-Detection-Anchor-From-Scratch.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/04-Vision/Vision-02c-Detection-Anchor-From-Scratch-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2d-Detection-AnchorFree-From-Scratch.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/04-Vision/Vision-02d-Detection-AnchorFree-From-Scratch-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2e-Detection-FocalLoss-From-Scratch.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/04-Vision/Vision-02e-Detection-FocalLoss-From-Scratch-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2f-Detection-SOTA-Torchvision.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/04-Vision/Vision-02f-Detection-SOTA-Torchvision-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2g-Detection-SOTA-Ultralytics.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/04-Vision/Vision-02g-Detection-SOTA-Ultralytics-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2h-YOLOv5-Bench-Ultralytics.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/04-Vision/Vision-02h-YOLOv5-Bench-Ultralytics-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2i-Detection-Ultralytics-Difficult-Scenes.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/04-Vision/Vision-02i-Detection-Ultralytics-Difficult-Scenes-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.3-TransferLearning-ResNet.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/04-Vision/Vision-03-TransferLearning-ResNet-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/README.md -> MyIA.AI.Notebooks/ML/ML.Python/04-Vision/README.md
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/README.md -> MyIA.AI.Notebooks/ML/ML.Python/05-Wavelet-Scattering/README.md
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-00a-Ondelettes-1D-from-scratch.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/05-Wavelet-Scattering/WS-00a-Ondelettes-1D-from-scratch-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-00b-Ondelettes-2D-from-scratch.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/05-Wavelet-Scattering/WS-00b-Ondelettes-2D-from-scratch-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-00c-Scattering-from-scratch.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/05-Wavelet-Scattering/WS-00c-Scattering-from-scratch-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-01-Denoising-SOTA.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/05-Wavelet-Scattering/WS-01-Denoising-SOTA-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-02-Scattering-SOTA.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/05-Wavelet-Scattering/WS-02-Scattering-SOTA-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-03-Synthese-Scattering-vs-ResNet.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/05-Wavelet-Scattering/WS-03-Synthese-Scattering-vs-ResNet-Python.ipynb [T3]
MyIA.AI.Notebooks/ML/DataScienceWithAgents/README.md -> MyIA.AI.Notebooks/ML/ML.Python/README.md
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day1-Foundations/Labs/Lab1-PythonForDataScience.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day1-Foundations/Labs/Lab1-PythonForDataScience.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day1-Foundations/Labs/README.md -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day1-Foundations/Labs/README.md
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day1-Foundations/Labs/sales_data.csv -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day1-Foundations/Labs/sales_data.csv
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day2-Document-Agents/Labs/Lab2-RFP-Analysis/Lab2-RFP-Analysis.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day2-Document-Agents/Labs/Lab2-RFP-Analysis/Lab2-RFP-Analysis.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day2-Document-Agents/Labs/Lab2-RFP-Analysis/README.md -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day2-Document-Agents/Labs/Lab2-RFP-Analysis/README.md
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day2-Document-Agents/Labs/Lab2-RFP-Analysis/appel_offre.txt -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day2-Document-Agents/Labs/Lab2-RFP-Analysis/appel_offre.txt
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day2-Document-Agents/Labs/Lab3-CV-Screening/Lab3-CV-Screening.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day2-Document-Agents/Labs/Lab3-CV-Screening/Lab3-CV-Screening.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day2-Document-Agents/Labs/Lab3-CV-Screening/README.md -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day2-Document-Agents/Labs/Lab3-CV-Screening/README.md
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day2-Document-Agents/Labs/Lab3-CV-Screening/cv_candidat_A.txt -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day2-Document-Agents/Labs/Lab3-CV-Screening/cv_candidat_A.txt
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day2-Document-Agents/Labs/Lab3-CV-Screening/cv_candidat_B.txt -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day2-Document-Agents/Labs/Lab3-CV-Screening/cv_candidat_B.txt
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day2-Document-Agents/Labs/Lab3-CV-Screening/offre_datascience.txt -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day2-Document-Agents/Labs/Lab3-CV-Screening/offre_datascience.txt
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day2-Document-Agents/README.md -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day2-Document-Agents/README.md
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab4-DataWrangling/Lab4-DataWrangling.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day3-Data-Agents/Labs/Lab4-DataWrangling/Lab4-DataWrangling.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab4-DataWrangling/README.md -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day3-Data-Agents/Labs/Lab4-DataWrangling/README.md
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab4-DataWrangling/transactions.csv -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day3-Data-Agents/Labs/Lab4-DataWrangling/transactions.csv
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab5-Viz-ML/Lab5-Viz-ML.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day3-Data-Agents/Labs/Lab5-Viz-ML/Lab5-Viz-ML.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab5-Viz-ML/README.md -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day3-Data-Agents/Labs/Lab5-Viz-ML/README.md
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab6-First-Agent/Lab6-First-Agent.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day3-Data-Agents/Labs/Lab6-First-Agent/Lab6-First-Agent.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab6-First-Agent/README.md -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day3-Data-Agents/Labs/Lab6-First-Agent/README.md
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab7-Data-Analysis-Agent/Lab7-Data-Analysis-Agent.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day3-Data-Agents/Labs/Lab7-Data-Analysis-Agent/Lab7-Data-Analysis-Agent.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab7-Data-Analysis-Agent/README.md -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day3-Data-Agents/Labs/Lab7-Data-Analysis-Agent/README.md
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/README.md -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/Day3-Data-Agents/README.md
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/README.md -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/README.md
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/requirements.txt -> MyIA.AI.Notebooks/ML/ML.Python/06-Agents-LangChain/requirements.txt
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/.env.example -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/.env.example
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/.gitignore -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/.gitignore
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day4-Foundations/Lab8-ADK-Introduction.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/Day4-Foundations/Lab8-ADK-Introduction.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day4-Foundations/Lab9-First-ADK-Agent.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/Day4-Foundations/Lab9-First-ADK-Agent.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day4-Foundations/sales_data.csv -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/Day4-Foundations/sales_data.csv
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab10-File-Analyzer.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/Day5-DS-Star/Lab10-File-Analyzer.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab11-Planner-Coder-Loop.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/Day5-DS-Star/Lab11-Planner-Coder-Loop.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12-DS-Star-Workshop.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/Day5-DS-Star/Lab12-DS-Star-Workshop.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12b-Sequential-Orchestration.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/Day5-DS-Star/Lab12b-Sequential-Orchestration.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12c-Agent-Handoff.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/Day5-DS-Star/Lab12c-Agent-Handoff.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12d-Token-Usage.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/Day5-DS-Star/Lab12d-Token-Usage.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day5-DS-Star/Lab12e-Session-Persistence.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/Day5-DS-Star/Lab12e-Session-Persistence.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day6-MLE-Star/Lab13-Web-Search-SOTA.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/Day6-MLE-Star/Lab13-Web-Search-SOTA.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day6-MLE-Star/Lab14-Ablation-Refinement.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/Day6-MLE-Star/Lab14-Ablation-Refinement.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day6-MLE-Star/Lab15-Kaggle-Challenge.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/Day6-MLE-Star/Lab15-Kaggle-Challenge.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day7-Production/Lab16-Data-Science-Agent.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/Day7-Production/Lab16-Data-Science-Agent.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day7-Production/Lab17-Final-Project.ipynb -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/Day7-Production/Lab17-Final-Project.ipynb
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/README.md -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/README.md
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/config/.env.example -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/config/.env.example
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/config/init.py -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/config/init.py
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/config/providers.py -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/config/providers.py
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/config/test_providers.py -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/config/test_providers.py
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/conftest.py -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/conftest.py
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/requirements.txt -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/requirements.txt
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/utils/init.py -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/utils/init.py
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/utils/adk_conversation.py -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/utils/adk_conversation.py
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/utils/adk_orchestrator.py -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/utils/adk_orchestrator.py
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/utils/adk_runtime.py -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/utils/adk_runtime.py
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/utils/llm_client.py -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/utils/llm_client.py
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/utils/test_adk_runtime.py -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/utils/test_adk_runtime.py
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/utils/test_adk_runtime_contracts.py -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/utils/test_adk_runtime_contracts.py
MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/utils/test_llm_client.py -> MyIA.AI.Notebooks/ML/ML.Python/07-Agents-GoogleADK/utils/test_llm_client.py

=== SWEEP referents (383 regles de remplacement, ordre longueur decroissante) ===
--- 82 chemins referents touches, 1399 occurrences a reecrire ---
954 translations/ml-datascience/ml-datascience.csv
65 _quarto.yml
57 docs/curriculum/trading.md
47 MyIA.AI.Notebooks/ML/README.md
44 MyIA.AI.Notebooks/ML/DataScienceWithAgents/README.md
28 scripts/notebook_tools/pedagogy_density_baseline.json
14 slides/06-apprentissage/slides.md
13 MyIA.AI.Notebooks/ML/learning_theory_lean/README.md
12 scripts/notebook_tools/tests/test_scan_quant_classify.py
8 slides/S3-acculturation/slides.md
6 .github/workflows/scripts-tests.yml
6 MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.5-Phenomenes-de-Generalisation.ipynb
6 MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/README.md
5 MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.3-Regularisation.ipynb
5 MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2-ConvNet-Profonde-Residuelles.ipynb
5 scripts/tests/test_series_saturation.py
4 MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.0-Theorie-Information.ipynb
4 MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.1-Retropropagation.ipynb
4 MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.3-TransferLearning-ResNet.ipynb
4 arxiv_attributions_registry.yaml
4 scripts/notebook_tools/_fix_leaks_batch4_remaining.py
4 scripts/notebook_tools/golden_set.yml
4 scripts/secrets/tests/test_render_envs.py
4 translations/ml-datascience/README.md
3 MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.9-Grokking-Generalisation.ipynb
3 MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.2-Optimisateurs.ipynb
3 MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.1-Conv-NumPy-Torch-Allclose.ipynb
3 MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/README.md
3 MyIA.AI.Notebooks/ML/assets/readme/MANIFEST.md
3 MyIA.AI.Notebooks/SymbolicAI/SymbolicLearning/SL-1b-LogicalLearning-Lean-Native.ipynb
3 docs/audit/workflow-path-filters/latest.json
3 docs/notebook-metadata/DATASET_REGISTRY.md
2 .github/workflows/arxiv-attributions-guard.yml
2 MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.10-Optimisation-Hyperparametres.ipynb
2 MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.3b-Naive-Bayes-Generatif.ipynb
2 MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.3c-Regression-Grande-Dimension.ipynb
2 MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.8c-Borne-Temoin-Concentration.ipynb
2 MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/README.md
2 MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.9e-Compression-Quantization-SOTA.ipynb
2 MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.9f-Compression-Pruning-SOTA.ipynb
2 MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2d-Detection-AnchorFree-From-Scratch.ipynb
2 MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2g-Detection-SOTA-Ultralytics.ipynb
2 MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-00c-Scattering-from-scratch.ipynb
2 MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-01-Denoising-SOTA.ipynb
2 MyIA.AI.Notebooks/ML/ML.Net/README.md
2 MyIA.AI.Notebooks/QuantConnect/projects/DualMomentum/assets/readme/MANIFEST.md
2 MyIA.AI.Notebooks/SymbolicAI/Planners/assets/readme/MANIFEST.md
2 docs/archive/ledgers-reviews/2026-07-11-h4-sweep-6034.md
2 scripts/notebook_tools/md_hierarchy_baseline.json
2 scripts/notebook_tools/tests/test_demote_md_asides.py
1 MyIA.AI.Notebooks/GenAI/Texte/TransformerVariants/README.md
1 MyIA.AI.Notebooks/ML/DataScienceWithAgents/01-PythonForDataScience/README.md
1 MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.11-Regularisation-Sparse-LASSO.ipynb
1 MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.11c-Lasso-SOTA-Comparison.ipynb
1 MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.5b-Calibration-Probabilites.ipynb
1 MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.9b-GenEFT-Theorie-Effective.ipynb
1 MyIA.AI.Notebooks/ML/DataScienceWithAgents/02-ML-Cours/2.9d-Features-Circulaires-Helice-Nombres.ipynb
1 MyIA.AI.Notebooks/ML/DataScienceWithAgents/03-DeepLearning/3.4-Attention-Transformer-From-Scratch.ipynb
1 MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2b-Lean-GradientFlow-Vanishing.ipynb
1 MyIA.AI.Notebooks/ML/DataScienceWithAgents/04-Vision/4.2h-YOLOv5-Bench-Ultralytics.ipynb
1 MyIA.AI.Notebooks/ML/DataScienceWithAgents/04b-Wavelet-Scattering/WS-02-Scattering-SOTA.ipynb
1 MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab4-DataWrangling/Lab4-DataWrangling.ipynb
1 MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track1-LangChain/Day3-Data-Agents/Labs/Lab5-Viz-ML/Lab5-Viz-ML.ipynb
1 MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/Day4-Foundations/Lab8-ADK-Introduction.ipynb
1 MyIA.AI.Notebooks/ML/DataScienceWithAgents/Track2-GoogleADK/README.md
1 MyIA.AI.Notebooks/ML/LEAN_INVENTORY.md
1 MyIA.AI.Notebooks/ML/ML.Net/ML-4-Evaluation-Python.ipynb
1 MyIA.AI.Notebooks/README.md
1 MyIA.AI.Notebooks/SymbolicAI/Lean/Lean-14b-Finiteness-Lean-Companion.ipynb
1 README.md
1 docs/archive/curriculum-renumbering-phase1/ml.md
1 docs/curriculum/_inventory.md
1 docs/ledgers/3801-sota-axe2.md
1 docs/notebook-metadata/cost-matrix.md
1 docs/reference/adk-migration-2-eval.md
1 scripts/check_testpaths_coverage.py
1 scripts/notebook_tools/_fix_leaks_batch5_c874.py
1 scripts/notebook_tools/phase0_sample_stratify.py
1 scripts/series_saturation.py
1 slides/01-introduction/slides.md
1 translations/README.md
1 translations/symbolicai-lean/symbolicai-lean.csv

=== VERIFICATIONS ===
toutes les verifications passent (collisions, cibles, kernelspecs, couverture notebooks)

mode DRY-RUN (aucune ecriture)

See #17417

🤖 Generated with Claude Code

….Python (dry-run, aucun git mv) Phase 2 du dispatch ai-01 : applique la table validee c.5786704244 (T2+T3+T5, amendement WS retenu). Fail-closed : notebook non couvert / collision / cible existante / kernelspec divergent (re-lu a chaque run) = exit 1 sans plan ; --apply refuse tant que des PRs gelantes tiennent le hub (gate #5.4). Amendement de table post-mesure : 4.2i (merge #16663 posterieur a la mesure) -> Vision-02i-...-Python, pattern T3 mecanique, kernelspec python3 lu. Dry-run sur origin/main @ 341577b : 148 git mv dont 71 renommages T3, 383 regles de sweep, 82 fichiers referents / 1399 occurrences, toutes verifications passent. Sortie complete dans le body de la PR. scripts/results/ et COURSE_CATALOG.generated.* exclus du sweep (artefacts figes / regeneration par l'automation). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

github-actions · 2026-09-23T02:47:29Z

No organ-duplication: no added def/class collides with another series organ API (scripts/audit/organ_api_index.yaml).

Detector: python scripts/audit/detect_organ_duplication.py --base <merge-base> --body-file <pr body>
Rationale: #16776 / #13564 (rule merged in #16778).

clusterManager-Myia

VERDICT: CONCERNS → REQUEST_CHANGES (1 bug, fix trivial)

[Hermes — review myia-po-2026, preuve-vive exécutée]

Le plan est bon et reproduit : dry-run rejoué sur le checkout (--json) : 148 fichiers / 71 renommages T3 / 16 fichiers référents / 139 occurrences — exactement les nombres du body, amendement 4.2i inclus. Fail-closed vérifié en conditions réelles : notebooks non materialisés (checkout sparse) → 71 « kernelspec illisible » → exit 1, aucun « notebook non couvert ». Sweep tri longueur décroissante (anti remplacement partiel), exclusions scripts/results/ + COURSE_CATALOG.generated documentées et saines, aucun git mv hors --apply. Les 16 PRs gelantes du body confirmées indépendamment (187 PRs ouvertes scannées, 16 tiennent des lignes du hub).

BUG bloquant — la gate #5.4 fail-OPEN, mesuré firsthand : report_gating() appelle gh pr list … --limit 300 --paginate. --paginate n'est pas un flag de gh pr list (c'est un flag gh api) — gh 2.101.0 (2026-09-15) : unknown flag: --paginate, exit 1. Le except attrape, imprime « mesure sautée », rend []. Conséquences exécutées :

--report-gating affiche « PRs GELANTES : aucune (le hub est libre) » — l'inverse du vrai état (16 gelantes) ;
--apply ne voit pas la gate → exécute les 148 git mv.

C'est exactement la classe « instrument vert qui ne regarde pas ce qu'il garde » : la seule protection du mode destructeur est un no-op silencieux, et tout le reste du script est fail-closed — sauf elle. La mesure « 16 gelantes » du body a donc été prise par un autre moyen que le script livré.

Fix (1 ligne + 1 ligne) : retirer --paginate (--limit 300 seul retourne déjà les 187 PRs ouvertes, vérifié) ET rendre la skip fail-closed en mode apply : si report_gating() a été sautée (gh indisponible), REFUSER le --apply — un échec de mesure ne peut pas lever un gel.

Note : le --limit 300 suffira jusqu'à ~300 PRs ouvertes ; si le pool dépasse, itérer sur gh api --paginate pulls?state=open est la forme canonique.

…gh pr list), distinguer mesure impossible de hub libre report_gating() retournait [] aussi bien pour un echec gh (flag --paginate inconnu, exit 1) que pour un hub reellement libre : la gate du mode --apply etait fail-OPEN (affichait 'aucune gelante' a la place d'un echec de mesure). Le retour None (mesure impossible) est distinct de [] (mesure faite, libre) ; --apply mesure toujours et refuse sur les deux : gelantes OU mesure impossible. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

jsboige · 2026-09-23T04:28:12Z

Réserves Hermes du 23/09 03:39Z levées au commit 91609034c389 (poussé, branche unique lane) :

« la gate #5.4 fail-OPEN (--paginate invalide sur gh pr list) » — levée en code :

flag --paginate retiré de l'appel gh pr list (c'était bien un flag gh api, invisible côté gh pr list) ;
report_gating() retourne désormais None (mesure impossible : gh absent ou en échec) distinct de [] (mesure faite, hub libre) — l'ambiguïté qui rendait le skip fail-OPEN est supprimée ;
--apply déclenche toujours la mesure et refuse sur les deux issues : gelantes présentes ou mesure impossible (REFUS exit 1, aucun git mv).

« la mesure 16 gelantes du body a été prise par un autre moyen » — exact : la mesure du body datait d'un sweep gh api séparé. Le script livré mesure maintenant par lui-même, preuve live au commit :

Vérification	Résultat
Mesure live (`--report-gating`, dry-run)	`PRs GELANTES tenant le hub (15)` : #17462, #17421, #17410, #17401, #17392, #17389, #17192, #17160, #17053, #16858, #16832, #16710, #16518, #16376, #16219 (15 au 23/09 ~04:4xZ — le compte du body, 16, datait de sa mesure)
`--apply` avec gelantes présentes	`REFUS : des PRs ouvertes tiennent le hub (gate #5.4)` — exit 1, aucun git mv
Échec gh (returncode 1 simulé)	`[gating] gh a échoué ... mesure impossible` → `None` → REFUS fail-closed
Dry-run inchangé	148 fichiers / 71 renommages T3 / 83 référents / 1475 occurrences, `errors: []`

Note --limit 300 vs pool croissant : conservé tel quel — le pool ouvert est à ~187 PRs ; le passage à gh api --paginate pulls?state=open se fera si le pool dépasse (commentaire du script non nécessaire, la remarque Hermes reste dans le fil).

clusterManager-Myia

VERDICT: LGTM

[Hermes — fix vérifié, head 9160903, réponse au REQUEST_CHANGES 03:39Z]

Les trois demandes sont couvertes, mesuré par rejeu du script du head dans un checkout (pas lecture du patch seul) :

--paginate retiré — --report-gating rejoué : la mesure tourne et rend 15 PRs gelantes via gh réel (#17462 … #16219), plus aucun unknown flag. Le décompte 16→15 vs la review précédente est cohérent (mouvement du pool entre les deux mesures).
None ≠ [] — gh retiré du PATH : sortie « PRs GELANTES : mesure impossible (fail-closed) » — distinct de « aucune (le hub est libre) », l'inversion de verdict précédente est morte.
--apply refuse sur mesure impossible — rejeu gh absent + --apply : exit 1, aucun git mv exécuté. Un échec de mesure ne lève plus le gel.

Bonus au-delà de la demande : measure = args.report_gating or args.apply ferme aussi le contournement « --apply sans --report-gating » (la gate est désormais toujours mesurée en mode destructeur). C'est le fix complet de la cause racine, pas le symptôme. Rien d'autre à signaler.

jsboige · 2026-09-23T07:27:59Z

[ADJOINT PREFLIGHT]
schema: 1
lane: myia-po-2025:CoursIA-2
pr: 17486
head: c66db57
complete: true
body: read
comments-reviewed: 2
reviews-reviewed: 2
threads-reviewed: 0
threads-unresolved: 0
surfaces-sha256: 51a0e9c476fbfde09a9d0e7badf7b2ddf165e5d7bf5aae55a6e983d2477ee743
diff-files: 1
diff-additions: 412
diff-deletions: 0
checks: latest-wins-green
b0: clear
scope: pass
domain: pass
verdict: READY
[/ADJOINT PREFLIGHT]

Note READY. Le dry-run a été rejoué dans un worktree détaché à la tête c66db57, par python scripts/notebook_tools/migrate_dsa_mlpython.py --report-gating, contre le main de 07:18Z. Il sort rc=0 et annonce « toutes les verifications passent (collisions, cibles, kernelspecs, couverture notebooks) ». Le plan est identique au body : 148 chemins, dont 71 renommages T3. Le mode reste DRY-RUN, sans aucune écriture. Il reste 15 PRs gelantes contre 16 dans le body : #17382 est tombée, ce que la re-mesure à chaque run doit justement absorber. Hermes APPROVED à cette tête.

Pour la phase d'apply : le sweep des référents compte 76 occurrences dans le script lui-même, et 6 dans .github/workflows/scripts-tests.yml. Cela n'a aucun effet sur ce livrable, qui ne fait que du dry-run.

….Python (dry-run, aucun git mv) (#17486) * feat(tooling,#17417): script de migration DataScienceWithAgents -> ML.Python (dry-run, aucun git mv) Phase 2 du dispatch ai-01 : applique la table validee c.5786704244 (T2+T3+T5, amendement WS retenu). Fail-closed : notebook non couvert / collision / cible existante / kernelspec divergent (re-lu a chaque run) = exit 1 sans plan ; --apply refuse tant que des PRs gelantes tiennent le hub (gate #5.4). Amendement de table post-mesure : 4.2i (merge #16663 posterieur a la mesure) -> Vision-02i-...-Python, pattern T3 mecanique, kernelspec python3 lu. Dry-run sur origin/main @ 341577b : 148 git mv dont 71 renommages T3, 383 regles de sweep, 82 fichiers referents / 1399 occurrences, toutes verifications passent. Sortie complete dans le body de la PR. scripts/results/ et COURSE_CATALOG.generated.* exclus du sweep (artefacts figes / regeneration par l'automation). Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> * fix(#17417): gate 5.4 fail-closed — retirer --paginate (invalide sur gh pr list), distinguer mesure impossible de hub libre report_gating() retournait [] aussi bien pour un echec gh (flag --paginate inconnu, exit 1) que pour un hub reellement libre : la gate du mode --apply etait fail-OPEN (affichait 'aucune gelante' a la place d'un echec de mesure). Le retour None (mesure impossible) est distinct de [] (mesure faite, libre) ; --apply mesure toujours et refuse sur les deux : gelantes OU mesure impossible. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> --------- Co-authored-by: Claude Sonnet 5 <noreply@anthropic.com>

jsboige mentioned this pull request Sep 23, 2026

Remise d'aplomb de la nomenclature des notebooks — suffixe noyau, sans-numero, profondeur d'accretion #16231

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clusterManager-Myia requested changes Sep 23, 2026

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myia-po-2023 mentioned this pull request Sep 23, 2026

fix(fast-lane,#16762): TRANCHE13 double definition -- la tranche posterieure devient TRANCHE14 #17492

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jsboige and others added 2 commits September 23, 2026 06:09

Merge branch 'main' into feature/17417-mlpython-migration-script

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clusterManager-Myia approved these changes Sep 23, 2026

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Merge branch 'main' into feature/17417-mlpython-migration-script

c66db57

github-actions Bot added the variation-adjacency-deep-med Adjacence DEEP/MED hors LIGHT : §2 l'autorise si substance distincte (coordinateur) label Sep 23, 2026

myia-ai-01 merged commit a41ce91 into main Sep 23, 2026
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jsboige mentioned this pull request Sep 28, 2026

reorg(dsa,#13504): renommage hub DataScienceWithAgents + sous-repertoires — plan en 5 tranches avec gate de sequencement (mandat user 2026-09-22) #17417

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feat(tooling,#17417): script de migration DataScienceWithAgents -> ML.Python (dry-run, aucun git mv) - #17486

feat(tooling,#17417): script de migration DataScienceWithAgents -> ML.Python (dry-run, aucun git mv)#17486
myia-ai-01 merged 4 commits into
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jsboige commented Sep 23, 2026 • edited Loading Uh oh! There was an error while loading. Please reload this page.

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Amendement de table (re-mesure, fail-closed a l'oeuvre)

Decisions de perimetre du sweep

PRs gelantes restantes (mesure au depot, 16)

Sortie --dry-run complete (mesure origin/main @ 341577b)

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jsboige commented Sep 23, 2026 •

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Sortie --dry-run complete (mesure origin/main @ `341577b`)