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CompPhysics · Apr 16, 2024 · c923370 · c923370
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diff --git a/doc/pub/week13/html/week13-bs.html b/doc/pub/week13/html/week13-bs.html
@@ -45,10 +45,10 @@
                2,
                None,
                'reminder-from-last-week-and-layout-of-lecture-this-week'),
-              ('Code for RBMs using PyTorch',
+              ('Code for RBMs using PyTorch for a binary-binary RBM',
                2,
                None,
-               'code-for-rbms-using-pytorch'),
+               'code-for-rbms-using-pytorch-for-a-binary-binary-rbm'),
               ('RBM using TensorFlow and Keras',
                2,
                None,
@@ -59,10 +59,10 @@
                'codes-for-energy-based-models'),
               ('Langevin sampling', 2, None, 'langevin-sampling'),
               ('More on the SGLD', 2, None, 'more-on-the-sgld'),
-              ('Code example of Langevin Samplig',
+              ('Code example of Langevin Sampling',
                2,
                None,
-               'code-example-of-langevin-samplig'),
+               'code-example-of-langevin-sampling'),
               ('Theory of Variational Autoencoders',
                2,
                None,
@@ -185,12 +185,12 @@
      <!-- navigation toc: --> <li><a href="#plans-for-the-week-april-15-19-2024" style="font-size: 80%;">Plans for the week April 15-19, 2024</a></li>
      <!-- navigation toc: --> <li><a href="#readings" style="font-size: 80%;">Readings</a></li>
      <!-- navigation toc: --> <li><a href="#reminder-from-last-week-and-layout-of-lecture-this-week" style="font-size: 80%;">Reminder from last week and layout of lecture this week</a></li>
-     <!-- navigation toc: --> <li><a href="#code-for-rbms-using-pytorch" style="font-size: 80%;">Code for RBMs using PyTorch</a></li>
+     <!-- navigation toc: --> <li><a href="#code-for-rbms-using-pytorch-for-a-binary-binary-rbm" style="font-size: 80%;">Code for RBMs using PyTorch for a binary-binary RBM</a></li>
      <!-- navigation toc: --> <li><a href="#rbm-using-tensorflow-and-keras" style="font-size: 80%;">RBM using TensorFlow and Keras</a></li>
      <!-- navigation toc: --> <li><a href="#codes-for-energy-based-models" style="font-size: 80%;">Codes for Energy-based models</a></li>
      <!-- navigation toc: --> <li><a href="#langevin-sampling" style="font-size: 80%;">Langevin sampling</a></li>
      <!-- navigation toc: --> <li><a href="#more-on-the-sgld" style="font-size: 80%;">More on the SGLD</a></li>
-     <!-- navigation toc: --> <li><a href="#code-example-of-langevin-samplig" style="font-size: 80%;">Code example of Langevin Samplig</a></li>
+     <!-- navigation toc: --> <li><a href="#code-example-of-langevin-sampling" style="font-size: 80%;">Code example of Langevin Sampling</a></li>
      <!-- navigation toc: --> <li><a href="#theory-of-variational-autoencoders" style="font-size: 80%;">Theory of Variational Autoencoders</a></li>
      <!-- navigation toc: --> <li><a href="#the-autoencoder-again" style="font-size: 80%;">The Autoencoder again</a></li>
      <!-- navigation toc: --> <li><a href="#schematic-image-of-an-autoencoder" style="font-size: 80%;">Schematic image of an Autoencoder</a></li>
@@ -310,7 +310,7 @@ <h2 id="reminder-from-last-week-and-layout-of-lecture-this-week" class="anchor">
 <li> Thereafter we start our discussions of Variational autoencoders and Generalized adversarial networks</li>
 </ol>
 <!-- !split -->
-<h2 id="code-for-rbms-using-pytorch" class="anchor">Code for RBMs using PyTorch </h2>
+<h2 id="code-for-rbms-using-pytorch-for-a-binary-binary-rbm" class="anchor">Code for RBMs using PyTorch for a binary-binary RBM </h2>
 
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
@@ -492,7 +492,7 @@ <h2 id="more-on-the-sgld" class="anchor">More on the SGLD </h2>
 </p>
 
 <!-- !split -->
-<h2 id="code-example-of-langevin-samplig" class="anchor">Code example of Langevin Samplig </h2>
+<h2 id="code-example-of-langevin-sampling" class="anchor">Code example of Langevin Sampling </h2>
 
 <p>In our calculations the gradient is calculated using the model we have
 for the probability distribution. For an energy-based model this gives

diff --git a/doc/pub/week13/html/week13-reveal.html b/doc/pub/week13/html/week13-reveal.html
@@ -239,7 +239,7 @@ <h2 id="reminder-from-last-week-and-layout-of-lecture-this-week">Reminder from l
 </section>
 
 <section>
-<h2 id="code-for-rbms-using-pytorch">Code for RBMs using PyTorch </h2>
+<h2 id="code-for-rbms-using-pytorch-for-a-binary-binary-rbm">Code for RBMs using PyTorch for a binary-binary RBM </h2>
 
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
@@ -428,7 +428,7 @@ <h2 id="more-on-the-sgld">More on the SGLD </h2>
 </section>
 
 <section>
-<h2 id="code-example-of-langevin-samplig">Code example of Langevin Samplig </h2>
+<h2 id="code-example-of-langevin-sampling">Code example of Langevin Sampling </h2>
 
 <p>In our calculations the gradient is calculated using the model we have
 for the probability distribution. For an energy-based model this gives

diff --git a/doc/pub/week13/html/week13-solarized.html b/doc/pub/week13/html/week13-solarized.html
@@ -72,10 +72,10 @@
                2,
                None,
                'reminder-from-last-week-and-layout-of-lecture-this-week'),
-              ('Code for RBMs using PyTorch',
+              ('Code for RBMs using PyTorch for a binary-binary RBM',
                2,
                None,
-               'code-for-rbms-using-pytorch'),
+               'code-for-rbms-using-pytorch-for-a-binary-binary-rbm'),
               ('RBM using TensorFlow and Keras',
                2,
                None,
@@ -86,10 +86,10 @@
                'codes-for-energy-based-models'),
               ('Langevin sampling', 2, None, 'langevin-sampling'),
               ('More on the SGLD', 2, None, 'more-on-the-sgld'),
-              ('Code example of Langevin Samplig',
+              ('Code example of Langevin Sampling',
                2,
                None,
-               'code-example-of-langevin-samplig'),
+               'code-example-of-langevin-sampling'),
               ('Theory of Variational Autoencoders',
                2,
                None,
@@ -258,7 +258,7 @@ <h2 id="reminder-from-last-week-and-layout-of-lecture-this-week">Reminder from l
 <li> Thereafter we start our discussions of Variational autoencoders and Generalized adversarial networks</li>
 </ol>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
-<h2 id="code-for-rbms-using-pytorch">Code for RBMs using PyTorch </h2>
+<h2 id="code-for-rbms-using-pytorch-for-a-binary-binary-rbm">Code for RBMs using PyTorch for a binary-binary RBM </h2>
 
 
 <!-- code=python (!bc pycod) typeset with pygments style "perldoc" -->
@@ -440,7 +440,7 @@ <h2 id="more-on-the-sgld">More on the SGLD </h2>
 </p>
 
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
-<h2 id="code-example-of-langevin-samplig">Code example of Langevin Samplig </h2>
+<h2 id="code-example-of-langevin-sampling">Code example of Langevin Sampling </h2>
 
 <p>In our calculations the gradient is calculated using the model we have
 for the probability distribution. For an energy-based model this gives

diff --git a/doc/pub/week13/html/week13.html b/doc/pub/week13/html/week13.html
@@ -149,10 +149,10 @@
                2,
                None,
                'reminder-from-last-week-and-layout-of-lecture-this-week'),
-              ('Code for RBMs using PyTorch',
+              ('Code for RBMs using PyTorch for a binary-binary RBM',
                2,
                None,
-               'code-for-rbms-using-pytorch'),
+               'code-for-rbms-using-pytorch-for-a-binary-binary-rbm'),
               ('RBM using TensorFlow and Keras',
                2,
                None,
@@ -163,10 +163,10 @@
                'codes-for-energy-based-models'),
               ('Langevin sampling', 2, None, 'langevin-sampling'),
               ('More on the SGLD', 2, None, 'more-on-the-sgld'),
-              ('Code example of Langevin Samplig',
+              ('Code example of Langevin Sampling',
                2,
                None,
-               'code-example-of-langevin-samplig'),
+               'code-example-of-langevin-sampling'),
               ('Theory of Variational Autoencoders',
                2,
                None,
@@ -335,7 +335,7 @@ <h2 id="reminder-from-last-week-and-layout-of-lecture-this-week">Reminder from l
 <li> Thereafter we start our discussions of Variational autoencoders and Generalized adversarial networks</li>
 </ol>
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
-<h2 id="code-for-rbms-using-pytorch">Code for RBMs using PyTorch </h2>
+<h2 id="code-for-rbms-using-pytorch-for-a-binary-binary-rbm">Code for RBMs using PyTorch for a binary-binary RBM </h2>
 
 
 <!-- code=python (!bc pycod) typeset with pygments style "default" -->
@@ -517,7 +517,7 @@ <h2 id="more-on-the-sgld">More on the SGLD </h2>
 </p>
 
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
-<h2 id="code-example-of-langevin-samplig">Code example of Langevin Samplig </h2>
+<h2 id="code-example-of-langevin-sampling">Code example of Langevin Sampling </h2>
 
 <p>In our calculations the gradient is calculated using the model we have
 for the probability distribution. For an energy-based model this gives

diff --git a/doc/pub/week13/ipynb/ipynb-week13-src.tar.gz b/doc/pub/week13/ipynb/ipynb-week13-src.tar.gz