diff --git a/doc/pub/week43/html/week43-bs.html b/doc/pub/week43/html/week43-bs.html
index a62aae7aa..c2f5398e8 100644
--- a/doc/pub/week43/html/week43-bs.html
+++ b/doc/pub/week43/html/week43-bs.html
@@ -8,8 +8,7 @@ doconce format html week43.do.txt --html_style=bootstrap --pygments_html_style=d
 <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
 <meta name="generator" content="DocOnce: https://github.com/doconce/doconce/" />
 <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-<meta name="description" content="Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis">
-<title>Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis</title>
+<title></title>
 <!-- Bootstrap style: bootstrap -->
 <!-- doconce format html week43.do.txt --html_style=bootstrap --pygments_html_style=default --html_admon=bootstrap_panel --html_output=week43-bs --no_mako -->
 <link href="https://netdna.bootstrapcdn.com/bootstrap/3.1.1/css/bootstrap.min.css" rel="stylesheet">
@@ -38,10 +37,6 @@ doconce format html week43.do.txt --html_style=bootstrap --pygments_html_style=d
 {'highest level': 2,
  'sections': [('Plans for week 43', 2, None, 'plans-for-week-43'),
               ('Reading Recommendations', 2, None, 'reading-recommendations'),
-              ('Summary on Deep Learning Methods',
-               2,
-               None,
-               'summary-on-deep-learning-methods'),
               ('CNNs in brief', 2, None, 'cnns-in-brief'),
               ('Recurrent neural networks: Overarching view',
                2,
@@ -169,93 +164,10 @@ MathJax.Hub.Config({
 </script>
 
 
-<!-- Bootstrap navigation bar -->
-<div class="navbar navbar-default navbar-fixed-top">
-  <div class="navbar-header">
-    <button type="button" class="navbar-toggle" data-toggle="collapse" data-target=".navbar-responsive-collapse">
-      <span class="icon-bar"></span>
-      <span class="icon-bar"></span>
-      <span class="icon-bar"></span>
-    </button>
-    <a class="navbar-brand" href="week43-bs.html">Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis</a>
-  </div>
-  <div class="navbar-collapse collapse navbar-responsive-collapse">
-    <ul class="nav navbar-nav navbar-right">
-      <li class="dropdown">
-        <a href="#" class="dropdown-toggle" data-toggle="dropdown">Contents <b class="caret"></b></a>
-        <ul class="dropdown-menu">
-     <!-- navigation toc: --> <li><a href="#plans-for-week-43" style="font-size: 80%;"><b>Plans for week 43</b></a></li>
-     <!-- navigation toc: --> <li><a href="#reading-recommendations" style="font-size: 80%;"><b>Reading Recommendations</b></a></li>
-     <!-- navigation toc: --> <li><a href="#summary-on-deep-learning-methods" style="font-size: 80%;"><b>Summary on Deep Learning Methods</b></a></li>
-     <!-- navigation toc: --> <li><a href="#cnns-in-brief" style="font-size: 80%;"><b>CNNs in brief</b></a></li>
-     <!-- navigation toc: --> <li><a href="#recurrent-neural-networks-overarching-view" style="font-size: 80%;"><b>Recurrent neural networks: Overarching view</b></a></li>
-     <!-- navigation toc: --> <li><a href="#set-up-of-an-rnn" style="font-size: 80%;"><b>Set up of an RNN</b></a></li>
-     <!-- navigation toc: --> <li><a href="#a-simple-example" style="font-size: 80%;"><b>A simple example</b></a></li>
-     <!-- navigation toc: --> <li><a href="#an-extrapolation-example" style="font-size: 80%;"><b>An extrapolation example</b></a></li>
-     <!-- navigation toc: --> <li><a href="#formatting-the-data" style="font-size: 80%;"><b>Formatting the Data</b></a></li>
-     <!-- navigation toc: --> <li><a href="#predicting-new-points-with-a-trained-recurrent-neural-network" style="font-size: 80%;"><b>Predicting New Points With A Trained Recurrent Neural Network</b></a></li>
-     <!-- navigation toc: --> <li><a href="#other-things-to-try" style="font-size: 80%;"><b>Other Things to Try</b></a></li>
-     <!-- navigation toc: --> <li><a href="#other-types-of-recurrent-neural-networks" style="font-size: 80%;"><b>Other Types of Recurrent Neural Networks</b></a></li>
-     <!-- navigation toc: --> <li><a href="#generative-models" style="font-size: 80%;"><b>Generative Models</b></a></li>
-     <!-- navigation toc: --> <li><a href="#generative-adversarial-networks" style="font-size: 80%;"><b>Generative Adversarial Networks</b></a></li>
-     <!-- navigation toc: --> <li><a href="#discriminator" style="font-size: 80%;"><b>Discriminator</b></a></li>
-     <!-- navigation toc: --> <li><a href="#learning-process" style="font-size: 80%;"><b>Learning Process</b></a></li>
-     <!-- navigation toc: --> <li><a href="#more-about-the-learning-process" style="font-size: 80%;"><b>More about the Learning Process</b></a></li>
-     <!-- navigation toc: --> <li><a href="#additional-references" style="font-size: 80%;"><b>Additional References</b></a></li>
-     <!-- navigation toc: --> <li><a href="#writing-our-first-generative-adversarial-network" style="font-size: 80%;"><b>Writing Our First Generative Adversarial Network</b></a></li>
-     <!-- navigation toc: --> <li><a href="#mnist-and-gans" style="font-size: 80%;"><b>MNIST and GANs</b></a></li>
-     <!-- navigation toc: --> <li><a href="#other-models" style="font-size: 80%;"><b>Other Models</b></a></li>
-     <!-- navigation toc: --> <li><a href="#training-step" style="font-size: 80%;"><b>Training Step</b></a></li>
-     <!-- navigation toc: --> <li><a href="#checkpoints" style="font-size: 80%;"><b>Checkpoints</b></a></li>
-     <!-- navigation toc: --> <li><a href="#exploring-the-latent-space" style="font-size: 80%;"><b>Exploring the Latent Space</b></a></li>
-     <!-- navigation toc: --> <li><a href="#getting-results" style="font-size: 80%;"><b>Getting Results</b></a></li>
-     <!-- navigation toc: --> <li><a href="#interpolating-between-mnist-digits" style="font-size: 80%;"><b>Interpolating Between MNIST Digits</b></a></li>
-     <!-- navigation toc: --> <li><a href="#basic-ideas-of-the-principal-component-analysis-pca" style="font-size: 80%;"><b>Basic ideas of the Principal Component Analysis (PCA)</b></a></li>
-     <!-- navigation toc: --> <li><a href="#introducing-the-covariance-and-correlation-functions" style="font-size: 80%;"><b>Introducing the Covariance and Correlation functions</b></a></li>
-     <!-- navigation toc: --> <li><a href="#more-on-the-covariance" style="font-size: 80%;"><b>More on the covariance</b></a></li>
-     <!-- navigation toc: --> <li><a href="#reminding-ourselves-about-linear-regression" style="font-size: 80%;"><b>Reminding ourselves about Linear Regression</b></a></li>
-     <!-- navigation toc: --> <li><a href="#simple-example" style="font-size: 80%;"><b>Simple Example</b></a></li>
-     <!-- navigation toc: --> <li><a href="#the-correlation-matrix" style="font-size: 80%;"><b>The Correlation Matrix</b></a></li>
-     <!-- navigation toc: --> <li><a href="#numpy-functionality" style="font-size: 80%;"><b>Numpy Functionality</b></a></li>
-     <!-- navigation toc: --> <li><a href="#correlation-matrix-again" style="font-size: 80%;"><b>Correlation Matrix again</b></a></li>
-     <!-- navigation toc: --> <li><a href="#using-pandas" style="font-size: 80%;"><b>Using Pandas</b></a></li>
-     <!-- navigation toc: --> <li><a href="#and-then-the-franke-function" style="font-size: 80%;"><b>And then the Franke Function</b></a></li>
-     <!-- navigation toc: --> <li><a href="#lnks-with-the-design-matrix" style="font-size: 80%;"><b>Lnks with the Design Matrix</b></a></li>
-     <!-- navigation toc: --> <li><a href="#computing-the-expectation-values" style="font-size: 80%;"><b>Computing the Expectation Values</b></a></li>
-     <!-- navigation toc: --> <li><a href="#towards-the-pca-theorem" style="font-size: 80%;"><b>Towards the PCA theorem</b></a></li>
-     <!-- navigation toc: --> <li><a href="#more-on-the-pca-theorem" style="font-size: 80%;"><b>More on the PCA Theorem</b></a></li>
-     <!-- navigation toc: --> <li><a href="#the-algorithm-before-the-theorem" style="font-size: 80%;"><b>The Algorithm before the Theorem</b></a></li>
-     <!-- navigation toc: --> <li><a href="#writing-our-own-pca-code" style="font-size: 80%;"><b>Writing our own PCA code</b></a></li>
-     <!-- navigation toc: --> <li><a href="#implementing-it" style="font-size: 80%;"><b>Implementing it</b></a></li>
-     <!-- navigation toc: --> <li><a href="#first-step" style="font-size: 80%;"><b>First Step</b></a></li>
-     <!-- navigation toc: --> <li><a href="#scaling" style="font-size: 80%;"><b>Scaling</b></a></li>
-     <!-- navigation toc: --> <li><a href="#centered-data" style="font-size: 80%;"><b>Centered Data</b></a></li>
-     <!-- navigation toc: --> <li><a href="#exploring" style="font-size: 80%;"><b>Exploring</b></a></li>
-     <!-- navigation toc: --> <li><a href="#diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components" style="font-size: 80%;"><b>Diagonalize the sample covariance matrix to obtain the principal components</b></a></li>
-     <!-- navigation toc: --> <li><a href="#collecting-all-steps" style="font-size: 80%;"><b>Collecting all Steps</b></a></li>
-     <!-- navigation toc: --> <li><a href="#classical-pca-theorem" style="font-size: 80%;"><b>Classical PCA Theorem</b></a></li>
-     <!-- navigation toc: --> <li><a href="#the-pca-theorem" style="font-size: 80%;"><b>The PCA Theorem</b></a></li>
-     <!-- navigation toc: --> <li><a href="#geometric-interpretation-and-link-with-singular-value-decomposition" style="font-size: 80%;"><b>Geometric Interpretation and link with Singular Value Decomposition</b></a></li>
-     <!-- navigation toc: --> <li><a href="#pca-and-scikit-learn" style="font-size: 80%;"><b>PCA and scikit-learn</b></a></li>
-     <!-- navigation toc: --> <li><a href="#back-to-the-cancer-data" style="font-size: 80%;"><b>Back to the Cancer Data</b></a></li>
-     <!-- navigation toc: --> <li><a href="#incremental-pca" style="font-size: 80%;"><b>Incremental PCA</b></a></li>
-     <!-- navigation toc: --> <li><a href="#randomized-pca" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;Randomized PCA</a></li>
-     <!-- navigation toc: --> <li><a href="#kernel-pca" style="font-size: 80%;">&nbsp;&nbsp;&nbsp;Kernel PCA</a></li>
-     <!-- navigation toc: --> <li><a href="#other-techniques" style="font-size: 80%;"><b>Other techniques</b></a></li>
-
-        </ul>
-      </li>
-    </ul>
-  </div>
-</div>
-</div> <!-- end of navigation bar -->
 <div class="container">
 <p>&nbsp;</p><p>&nbsp;</p><p>&nbsp;</p> <!-- add vertical space -->
 <!-- ------------------- main content ---------------------- -->
-<div class="jumbotron">
-<center>
-<h1>Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis</h1>
-</center>  <!-- document title -->
+<p>ATITLE: Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis
 
 <!-- author(s): Morten Hjorth-Jensen -->
 <center>
@@ -270,19 +182,17 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Aug 23, 2022</h4>
+<h4>Oct 22, 2022</h4>
 </center> <!-- date -->
 <br>
-
-<!-- potential-jumbotron-button -->
-</div> <!-- end jumbotron -->
+</p>
 
 <!-- !split -->
 <h2 id="plans-for-week-43" class="anchor">Plans for week 43 </h2>
 
 <ul>
-<li> Thursday: Summary of Convolutional Neural Networks from week 42 and Recurrent Neural Networks</li>
-<li> Friday: Recurrent Neural Networks and other Deep Learning methods such as Generalized Adversarial Neural Networks. Start discussing Principal component analysis</li>
+<li> Thursday: Convolutional Neural Networks, basic elements and</li> 
+<li> Friday: Recurrent Neural Networks and other Deep, Generalized Adversarial Neural Networ and autoencoders</li>
 </ul>
 <div class="panel panel-default">
 <div class="panel-body">
@@ -314,13 +224,6 @@ MathJax.Hub.Config({
 <li> Goodfellow et al, chapter 10 on Recurrent NNs, chapters 11 and 12 on various practicalities around deep learning are also recommended.</li>
 <li> Aurelien Geron, chapter 14 on RNNs.</li>
 </ul>
-<!-- !split -->
-<h2 id="summary-on-deep-learning-methods" class="anchor">Summary on Deep Learning Methods </h2>
-
-<p>We have studied fully connected neural networks (also called artifical nueral networks) and convolutional neural networks (CNNs).</p>
-
-<p>The first type of deep learning networks work very well on homogeneous and structured input data while CCNs are normally tailored to recognizing images.</p>
-
 <!-- !split -->
 <h2 id="cnns-in-brief" class="anchor">CNNs in brief </h2>
 
diff --git a/doc/pub/week43/html/week43-reveal.html b/doc/pub/week43/html/week43-reveal.html
index dccbf2f00..02d1bfe16 100644
--- a/doc/pub/week43/html/week43-reveal.html
+++ b/doc/pub/week43/html/week43-reveal.html
@@ -9,8 +9,7 @@ doconce format html week43-reveal.html week43-reveal reveal --html_slide_theme=b
 <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
 <meta name="generator" content="DocOnce: https://github.com/doconce/doconce/" />
 <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-<meta name="description" content="Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis">
-<title>Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis</title>
+<title></title>
 
 <!-- reveal.js: https://lab.hakim.se/reveal-js/ -->
 
@@ -167,9 +166,7 @@ MathJax.Hub.Config({
 
 <section>
 <!-- ------------------- main content ---------------------- -->
-<center>
-<h1 style="text-align: center;">Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis</h1>
-</center>  <!-- document title -->
+<p>ATITLE: Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis
 
 <!-- author(s): Morten Hjorth-Jensen -->
 <center>
@@ -184,9 +181,10 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Aug 23, 2022</h4>
+<h4>Oct 22, 2022</h4>
 </center> <!-- date -->
 <br>
+</p>
 
 
 <center style="font-size:80%">
@@ -198,8 +196,8 @@ MathJax.Hub.Config({
 <h2 id="plans-for-week-43">Plans for week 43 </h2>
 
 <ul>
-<p><li> Thursday: Summary of Convolutional Neural Networks from week 42 and Recurrent Neural Networks</li>
-<p><li> Friday: Recurrent Neural Networks and other Deep Learning methods such as Generalized Adversarial Neural Networks. Start discussing Principal component analysis</li>
+<p><li> Thursday: Convolutional Neural Networks, basic elements and</li> 
+<p><li> Friday: Recurrent Neural Networks and other Deep, Generalized Adversarial Neural Networ and autoencoders</li>
 </ul>
 <p>
 <div class="alert alert-block alert-block alert-text-normal">
@@ -232,14 +230,6 @@ MathJax.Hub.Config({
 </ul>
 </section>
 
-<section>
-<h2 id="summary-on-deep-learning-methods">Summary on Deep Learning Methods </h2>
-
-<p>We have studied fully connected neural networks (also called artifical nueral networks) and convolutional neural networks (CNNs).</p>
-
-<p>The first type of deep learning networks work very well on homogeneous and structured input data while CCNs are normally tailored to recognizing images.</p>
-</section>
-
 <section>
 <h2 id="cnns-in-brief">CNNs in brief </h2>
 
diff --git a/doc/pub/week43/html/week43-solarized.html b/doc/pub/week43/html/week43-solarized.html
index 883ebf249..c114e8c0b 100644
--- a/doc/pub/week43/html/week43-solarized.html
+++ b/doc/pub/week43/html/week43-solarized.html
@@ -8,8 +8,7 @@ doconce format html week43.do.txt --pygments_html_style=perldoc --html_style=sol
 <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
 <meta name="generator" content="DocOnce: https://github.com/doconce/doconce/" />
 <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-<meta name="description" content="Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis">
-<title>Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis</title>
+<title></title>
 <link href="https://cdn.rawgit.com/doconce/doconce/master/bundled/html_styles/style_solarized_box/css/solarized_light_code.css" rel="stylesheet" type="text/css" title="light"/>
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 <script>hljs.initHighlightingOnLoad();</script>
@@ -65,10 +64,6 @@ div.toc p,a {
 {'highest level': 2,
  'sections': [('Plans for week 43', 2, None, 'plans-for-week-43'),
               ('Reading Recommendations', 2, None, 'reading-recommendations'),
-              ('Summary on Deep Learning Methods',
-               2,
-               None,
-               'summary-on-deep-learning-methods'),
               ('CNNs in brief', 2, None, 'cnns-in-brief'),
               ('Recurrent neural networks: Overarching view',
                2,
@@ -197,9 +192,7 @@ MathJax.Hub.Config({
 
 
 <!-- ------------------- main content ---------------------- -->
-<center>
-<h1>Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis</h1>
-</center>  <!-- document title -->
+<p>ATITLE: Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis
 
 <!-- author(s): Morten Hjorth-Jensen -->
 <center>
@@ -214,16 +207,17 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Aug 23, 2022</h4>
+<h4>Oct 22, 2022</h4>
 </center> <!-- date -->
 <br>
+</p>
 
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="plans-for-week-43">Plans for week 43 </h2>
 
 <ul>
-<li> Thursday: Summary of Convolutional Neural Networks from week 42 and Recurrent Neural Networks</li>
-<li> Friday: Recurrent Neural Networks and other Deep Learning methods such as Generalized Adversarial Neural Networks. Start discussing Principal component analysis</li>
+<li> Thursday: Convolutional Neural Networks, basic elements and</li> 
+<li> Friday: Recurrent Neural Networks and other Deep, Generalized Adversarial Neural Networ and autoencoders</li>
 </ul>
 <div class="alert alert-block alert-block alert-text-normal">
 <b>Excellent lectures on CNNs and RNNs</b>
@@ -253,13 +247,6 @@ MathJax.Hub.Config({
 <li> Goodfellow et al, chapter 10 on Recurrent NNs, chapters 11 and 12 on various practicalities around deep learning are also recommended.</li>
 <li> Aurelien Geron, chapter 14 on RNNs.</li>
 </ul>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
-<h2 id="summary-on-deep-learning-methods">Summary on Deep Learning Methods </h2>
-
-<p>We have studied fully connected neural networks (also called artifical nueral networks) and convolutional neural networks (CNNs).</p>
-
-<p>The first type of deep learning networks work very well on homogeneous and structured input data while CCNs are normally tailored to recognizing images.</p>
-
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="cnns-in-brief">CNNs in brief </h2>
 
diff --git a/doc/pub/week43/html/week43.html b/doc/pub/week43/html/week43.html
index d3ac5fdbe..dd0940ed0 100644
--- a/doc/pub/week43/html/week43.html
+++ b/doc/pub/week43/html/week43.html
@@ -8,8 +8,7 @@ doconce format html week43.do.txt --pygments_html_style=default --html_style=blo
 <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
 <meta name="generator" content="DocOnce: https://github.com/doconce/doconce/" />
 <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-<meta name="description" content="Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis">
-<title>Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis</title>
+<title></title>
 <style type="text/css">
 /* bloodish style */
 body {
@@ -142,10 +141,6 @@ div.toc p,a {
 {'highest level': 2,
  'sections': [('Plans for week 43', 2, None, 'plans-for-week-43'),
               ('Reading Recommendations', 2, None, 'reading-recommendations'),
-              ('Summary on Deep Learning Methods',
-               2,
-               None,
-               'summary-on-deep-learning-methods'),
               ('CNNs in brief', 2, None, 'cnns-in-brief'),
               ('Recurrent neural networks: Overarching view',
                2,
@@ -274,9 +269,7 @@ MathJax.Hub.Config({
 
 
 <!-- ------------------- main content ---------------------- -->
-<center>
-<h1>Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis</h1>
-</center>  <!-- document title -->
+<p>ATITLE: Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis
 
 <!-- author(s): Morten Hjorth-Jensen -->
 <center>
@@ -291,16 +284,17 @@ MathJax.Hub.Config({
 </center>
 <br>
 <center>
-<h4>Aug 23, 2022</h4>
+<h4>Oct 22, 2022</h4>
 </center> <!-- date -->
 <br>
+</p>
 
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="plans-for-week-43">Plans for week 43 </h2>
 
 <ul>
-<li> Thursday: Summary of Convolutional Neural Networks from week 42 and Recurrent Neural Networks</li>
-<li> Friday: Recurrent Neural Networks and other Deep Learning methods such as Generalized Adversarial Neural Networks. Start discussing Principal component analysis</li>
+<li> Thursday: Convolutional Neural Networks, basic elements and</li> 
+<li> Friday: Recurrent Neural Networks and other Deep, Generalized Adversarial Neural Networ and autoencoders</li>
 </ul>
 <div class="alert alert-block alert-block alert-text-normal">
 <b>Excellent lectures on CNNs and RNNs</b>
@@ -330,13 +324,6 @@ MathJax.Hub.Config({
 <li> Goodfellow et al, chapter 10 on Recurrent NNs, chapters 11 and 12 on various practicalities around deep learning are also recommended.</li>
 <li> Aurelien Geron, chapter 14 on RNNs.</li>
 </ul>
-<!-- !split --><br><br><br><br><br><br><br><br><br><br>
-<h2 id="summary-on-deep-learning-methods">Summary on Deep Learning Methods </h2>
-
-<p>We have studied fully connected neural networks (also called artifical nueral networks) and convolutional neural networks (CNNs).</p>
-
-<p>The first type of deep learning networks work very well on homogeneous and structured input data while CCNs are normally tailored to recognizing images.</p>
-
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="cnns-in-brief">CNNs in brief </h2>
 
diff --git a/doc/pub/week43/ipynb/ipynb-week43-src.tar.gz b/doc/pub/week43/ipynb/ipynb-week43-src.tar.gz
index 880b90f5e..0150afb5b 100644
Binary files a/doc/pub/week43/ipynb/ipynb-week43-src.tar.gz and b/doc/pub/week43/ipynb/ipynb-week43-src.tar.gz differ
diff --git a/doc/pub/week43/ipynb/week43.ipynb b/doc/pub/week43/ipynb/week43.ipynb
index 37e16f056..107eb14cd 100644
--- a/doc/pub/week43/ipynb/week43.ipynb
+++ b/doc/pub/week43/ipynb/week43.ipynb
@@ -2,43 +2,34 @@
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-   "cell_type": "markdown",
-   "id": "afd99353",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "# Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis\n",
+    "ATITLE: Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis\n",
+    "\n",
     "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
     "\n",
-    "Date: **Aug 23, 2022**\n",
+    "Date: **Oct 22, 2022**\n",
     "\n",
     "Copyright 1999-2022, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "c6a45fc7",
+   "id": "47ebfa02",
    "metadata": {
     "editable": true
    },
    "source": [
     "## Plans for week 43\n",
     "\n",
-    "* Thursday: Summary of Convolutional Neural Networks from week 42 and Recurrent Neural Networks\n",
+    "* Thursday: Convolutional Neural Networks, basic elements and \n",
     "\n",
-    "* Friday: Recurrent Neural Networks and other Deep Learning methods such as Generalized Adversarial Neural Networks. Start discussing Principal component analysis\n",
+    "* Friday: Recurrent Neural Networks and other Deep, Generalized Adversarial Neural Networ and autoencoders\n",
     "\n",
     "**Excellent lectures on CNNs and RNNs.**\n",
     "\n",
@@ -57,7 +48,7 @@
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   {
    "cell_type": "markdown",
-   "id": "d7657561",
+   "id": "1ac26ecb",
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@@ -71,21 +62,7 @@
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   {
    "cell_type": "markdown",
-   "id": "f5772403",
-   "metadata": {
-    "editable": true
-   },
-   "source": [
-    "## Summary on Deep Learning Methods\n",
-    "\n",
-    "We have studied fully connected neural networks (also called artifical nueral networks) and convolutional neural networks (CNNs).\n",
-    "\n",
-    "The first type of deep learning networks work very well on homogeneous and structured input data while CCNs are normally tailored to recognizing images."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bd4e5ace",
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@@ -1101,7 +1078,7 @@
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@@ -1111,7 +1088,7 @@
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@@ -1131,7 +1108,7 @@
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@@ -1143,7 +1120,7 @@
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@@ -1161,7 +1138,7 @@
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@@ -1173,7 +1150,7 @@
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@@ -1194,7 +1171,7 @@
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@@ -1211,7 +1188,7 @@
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@@ -1229,7 +1206,7 @@
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@@ -1292,7 +1269,7 @@
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@@ -1458,7 +1435,7 @@
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@@ -2203,7 +2180,7 @@
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@@ -2215,7 +2192,7 @@
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@@ -2228,7 +2205,7 @@
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@@ -2242,7 +2219,7 @@
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@@ -2252,7 +2229,7 @@
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@@ -2264,7 +2241,7 @@
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@@ -2283,7 +2260,7 @@
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@@ -2295,7 +2272,7 @@
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@@ -2307,7 +2284,7 @@
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@@ -2317,7 +2294,7 @@
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@@ -2329,7 +2306,7 @@
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@@ -2343,7 +2320,7 @@
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@@ -2362,7 +2339,7 @@
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@@ -2374,7 +2351,7 @@
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@@ -2393,7 +2370,7 @@
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@@ -2409,7 +2386,7 @@
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@@ -2427,7 +2404,7 @@
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@@ -2442,7 +2419,7 @@
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@@ -2463,7 +2440,7 @@
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@@ -2480,7 +2457,7 @@
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@@ -2512,7 +2489,7 @@
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@@ -2526,7 +2503,7 @@
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@@ -2539,7 +2516,7 @@
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@@ -2563,7 +2540,7 @@
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@@ -2576,7 +2553,7 @@
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@@ -2630,7 +2607,7 @@
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@@ -2645,7 +2622,7 @@
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@@ -2657,7 +2634,7 @@
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@@ -2669,7 +2646,7 @@
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@@ -2679,7 +2656,7 @@
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@@ -2696,7 +2673,7 @@
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@@ -2708,7 +2685,7 @@
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@@ -2723,7 +2700,7 @@
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@@ -2733,7 +2710,7 @@
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@@ -2747,7 +2724,7 @@
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@@ -2759,7 +2736,7 @@
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@@ -2771,7 +2748,7 @@
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@@ -2783,7 +2760,7 @@
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@@ -2798,7 +2775,7 @@
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@@ -2810,7 +2787,7 @@
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@@ -2820,7 +2797,7 @@
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@@ -2832,7 +2809,7 @@
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@@ -2842,7 +2819,7 @@
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@@ -2854,7 +2831,7 @@
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@@ -2879,7 +2856,7 @@
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@@ -2892,7 +2869,7 @@
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@@ -2911,7 +2888,7 @@
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@@ -2929,7 +2906,7 @@
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@@ -2942,7 +2919,7 @@
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@@ -2956,7 +2933,7 @@
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@@ -2968,7 +2945,7 @@
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@@ -2981,7 +2958,7 @@
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@@ -3000,7 +2977,7 @@
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@@ -3010,7 +2987,7 @@
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@@ -3022,7 +2999,7 @@
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@@ -3034,7 +3011,7 @@
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@@ -3044,7 +3021,7 @@
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@@ -3056,7 +3033,7 @@
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@@ -3070,7 +3047,7 @@
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@@ -3086,7 +3063,7 @@
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@@ -3105,7 +3082,7 @@
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@@ -3117,7 +3094,7 @@
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@@ -3129,7 +3106,7 @@
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@@ -3141,7 +3118,7 @@
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@@ -3154,7 +3131,7 @@
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@@ -3166,7 +3143,7 @@
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-   "id": "18ef1d25",
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@@ -3190,7 +3167,7 @@
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@@ -3203,7 +3180,7 @@
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@@ -3227,7 +3204,7 @@
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@@ -3239,7 +3216,7 @@
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@@ -3249,7 +3226,7 @@
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@@ -3267,7 +3244,7 @@
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@@ -3299,7 +3276,7 @@
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@@ -3309,7 +3286,7 @@
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@@ -3333,7 +3310,7 @@
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@@ -3357,7 +3334,7 @@
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@@ -3369,7 +3346,7 @@
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@@ -3379,7 +3356,7 @@
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@@ -3391,7 +3368,7 @@
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@@ -3401,7 +3378,7 @@
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@@ -3413,7 +3390,7 @@
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@@ -3423,7 +3400,7 @@
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@@ -3435,7 +3412,7 @@
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@@ -3461,7 +3438,7 @@
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@@ -3480,7 +3457,7 @@
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-   "id": "fbeb6903",
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@@ -3515,7 +3492,7 @@
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@@ -3532,7 +3509,7 @@
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@@ -3545,7 +3522,7 @@
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@@ -3560,7 +3537,7 @@
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@@ -3576,7 +3553,7 @@
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@@ -3589,7 +3566,7 @@
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@@ -3601,7 +3578,7 @@
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@@ -3613,7 +3590,7 @@
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@@ -3626,7 +3603,7 @@
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@@ -3665,7 +3642,7 @@
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@@ -3683,7 +3660,7 @@
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@@ -3698,7 +3675,7 @@
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@@ -3711,7 +3688,7 @@
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@@ -3724,7 +3701,7 @@
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@@ -3740,7 +3717,7 @@
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@@ -3755,7 +3732,7 @@
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   {
    "cell_type": "markdown",
-   "id": "524b0de1",
+   "id": "6c65a481",
    "metadata": {
     "editable": true
    },
@@ -3776,7 +3753,7 @@
   {
    "cell_type": "code",
    "execution_count": 48,
-   "id": "9106daee",
+   "id": "3833653d",
    "metadata": {
     "collapsed": false,
     "editable": true
@@ -3790,7 +3767,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "1fab3a71",
+   "id": "6a395fcb",
    "metadata": {
     "editable": true
    },
diff --git a/doc/src/week43/week43.do.txt b/doc/src/week43/week43.do.txt
index 21fb1f4af..303a0c840 100644
--- a/doc/src/week43/week43.do.txt
+++ b/doc/src/week43/week43.do.txt
@@ -1,12 +1,12 @@
-TITLE: Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis
+ATITLE: Week 43: Deep Learning: Recurrent Neural Networks and other Deep Learning Methods. Principal Component analysis
 AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
 DATE: today
 
 !split
 ===== Plans for week 43 =====
 
-* Thursday: Summary of Convolutional Neural Networks from week 42 and Recurrent Neural Networks
-* Friday: Recurrent Neural Networks and other Deep Learning methods such as Generalized Adversarial Neural Networks. Start discussing Principal component analysis
+* Thursday: Convolutional Neural Networks, basic elements and 
+* Friday: Recurrent Neural Networks and other Deep, Generalized Adversarial Neural Networ and autoencoders
 
 
 
@@ -29,12 +29,6 @@ DATE: today
 * Aurelien Geron, chapter 14 on RNNs.
 
 
-!split
-===== Summary on Deep Learning Methods =====
-
-We have studied fully connected neural networks (also called artifical nueral networks) and convolutional neural networks (CNNs).
-
-The first type of deep learning networks work very well on homogeneous and structured input data while CCNs are normally tailored to recognizing images.
 
 !split
 ===== CNNs in brief =====