correcting typos

Author: mhjensen

doc/pub/week16/html/week16-bs.html

@@ -913,9 +913,7 @@ <h2 id="summary-of-steps-in-pennylane-implementation" class="anchor">Summary of
 
 <ol>
 <li> Define a quantum device (qml.device) with enough wires for visibles+hidden.</li>
-</ol>
-<p>0 Construct a parametric quantum circuit (ansatz) that depends on trainable parameters (e.g. angles in rotations and entangling gates).</p>
-<ol>
+<li> Construct a parametric quantum circuit (ansatz) that depends on trainable parameters (e.g. angles in rotations and entangling gates).</li>
 <li> If modeling an RQBM, one might clamp visible qubits (preparing them according to training data) and apply gates only to hidden qubits.</li>
 <li> Measure relevant observables: one can return probabilities (probs) or expectation values (expval) of Pauli operators, depending on the chosen loss function.</li>
 <li> Define a cost function, such as the negative log-likelihood or a distance between output and target distribution.</li>

doc/pub/week16/html/week16-reveal.html

@@ -991,10 +991,7 @@ <h2 id="summary-of-steps-in-pennylane-implementation">Summary of steps in PennyL
 
 <ol>
 <p><li> Define a quantum device (qml.device) with enough wires for visibles+hidden.</li>
-</ol>
-<p>
-<p>0 Construct a parametric quantum circuit (ansatz) that depends on trainable parameters (e.g. angles in rotations and entangling gates).</p>
-<ol>
+<p><li> Construct a parametric quantum circuit (ansatz) that depends on trainable parameters (e.g. angles in rotations and entangling gates).</li>
 <p><li> If modeling an RQBM, one might clamp visible qubits (preparing them according to training data) and apply gates only to hidden qubits.</li>
 <p><li> Measure relevant observables: one can return probabilities (probs) or expectation values (expval) of Pauli operators, depending on the chosen loss function.</li>
 <p><li> Define a cost function, such as the negative log-likelihood or a distance between output and target distribution.</li>

doc/pub/week16/html/week16-solarized.html

@@ -879,9 +879,7 @@ <h2 id="summary-of-steps-in-pennylane-implementation">Summary of steps in PennyL
 
 <ol>
 <li> Define a quantum device (qml.device) with enough wires for visibles+hidden.</li>
-</ol>
-<p>0 Construct a parametric quantum circuit (ansatz) that depends on trainable parameters (e.g. angles in rotations and entangling gates).</p>
-<ol>
+<li> Construct a parametric quantum circuit (ansatz) that depends on trainable parameters (e.g. angles in rotations and entangling gates).</li>
 <li> If modeling an RQBM, one might clamp visible qubits (preparing them according to training data) and apply gates only to hidden qubits.</li>
 <li> Measure relevant observables: one can return probabilities (probs) or expectation values (expval) of Pauli operators, depending on the chosen loss function.</li>
 <li> Define a cost function, such as the negative log-likelihood or a distance between output and target distribution.</li>

doc/pub/week16/html/week16.html

@@ -956,9 +956,7 @@ <h2 id="summary-of-steps-in-pennylane-implementation">Summary of steps in PennyL
 
 <ol>
 <li> Define a quantum device (qml.device) with enough wires for visibles+hidden.</li>
-</ol>
-<p>0 Construct a parametric quantum circuit (ansatz) that depends on trainable parameters (e.g. angles in rotations and entangling gates).</p>
-<ol>
+<li> Construct a parametric quantum circuit (ansatz) that depends on trainable parameters (e.g. angles in rotations and entangling gates).</li>
 <li> If modeling an RQBM, one might clamp visible qubits (preparing them according to training data) and apply gates only to hidden qubits.</li>
 <li> Measure relevant observables: one can return probabilities (probs) or expectation values (expval) of Pauli operators, depending on the chosen loss function.</li>
 <li> Define a cost function, such as the negative log-likelihood or a distance between output and target distribution.</li>