correcting typos

Author: mhjensen

doc/pub/week41/html/week41-bs.html

@@ -44,6 +44,7 @@
                2,
                None,
                'material-for-the-lecture-on-monday-october-6-2025'),
+              ('Readings and Videos:', 2, None, 'readings-and-videos'),
               ('Mathematics of deep learning',
                2,
                None,
@@ -330,6 +331,7 @@
         <ul class="dropdown-menu">
      <!-- navigation toc: --> <li><a href="#plan-for-week-41-october-6-10" style="font-size: 80%;">Plan for week 41, October 6-10</a></li>
      <!-- navigation toc: --> <li><a href="#material-for-the-lecture-on-monday-october-6-2025" style="font-size: 80%;">Material for the lecture on Monday October 6, 2025</a></li>
+     <!-- navigation toc: --> <li><a href="#readings-and-videos" style="font-size: 80%;">Readings and Videos:</a></li>
      <!-- navigation toc: --> <li><a href="#mathematics-of-deep-learning" style="font-size: 80%;">Mathematics of deep learning</a></li>
      <!-- navigation toc: --> <li><a href="#reminder-on-books-with-hands-on-material-and-codes" style="font-size: 80%;">Reminder on books with hands-on material and codes</a></li>
      <!-- navigation toc: --> <li><a href="#lab-sessions-on-tuesday-and-wednesday" style="font-size: 80%;">Lab sessions on Tuesday and Wednesday</a></li>
@@ -452,24 +454,24 @@ <h2 id="material-for-the-lecture-on-monday-october-6-2025" class="anchor">Materi
 <ol>
 <li> Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.</li>
 <li> Building our own Feed-forward Neural Network, getting started
-<!-- * Video of lecture notes at <a href="https://youtu.be/pMRUbf9E-gM" target="_self"><tt>https://youtu.be/pMRUbf9E-gM</tt></a> -->
-<!-- * Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf" target="_self"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf</tt></a> --></li>
+<!-- * Video of lecture notes at URL:"" -->
+<!-- * Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek41.pdf" target="_self"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek41.pdf</tt></a> --></li>
 </ol>
+<!-- !split -->
+<h2 id="readings-and-videos" class="anchor">Readings and Videos: </h2>
 <div class="panel panel-default">
 <div class="panel-body">
 <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
 <ol>
 <li> These lecture notes</li>
 <li> For neural networks we recommend Goodfellow et al chapters 6 and 7.</li>
-<li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_self">GitHub</a>
-<ol type="a"></li>
+<li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_self">GitHub</a></li>
 <li> Neural Networks demystified at <a href="https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs" target="_self"><tt>https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs</tt></a></li>
-</ol>
 <li> Building Neural Networks from scratch at <a href="https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex" target="_self"><tt>https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex</tt></a></li>
 <li> Video on Neural Networks at <a href="https://www.youtube.com/watch?v=CqOfi41LfDw" target="_self"><tt>https://www.youtube.com/watch?v=CqOfi41LfDw</tt></a></li>
 <li> Video on the back propagation algorithm at <a href="https://www.youtube.com/watch?v=Ilg3gGewQ5U" target="_self"><tt>https://www.youtube.com/watch?v=Ilg3gGewQ5U</tt></a></li>
+<li> We also  recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at <a href="http://neuralnetworksanddeeplearning.com/chap4.html" target="_self"><tt>http://neuralnetworksanddeeplearning.com/chap4.html</tt></a>.</li>
 </ol>
-<p>We also  recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at <a href="http://neuralnetworksanddeeplearning.com/chap4.html" target="_self"><tt>http://neuralnetworksanddeeplearning.com/chap4.html</tt></a>.</p>
 </div>
 </div>
 
@@ -493,19 +495,18 @@ <h2 id="reminder-on-books-with-hands-on-material-and-codes" class="anchor">Remin
 <div class="panel panel-default">
 <div class="panel-body">
 <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
-<ul>
-<li> <a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_self">Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch</a></li>
-</ul>
+<a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_self">Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch</a>
 </div>
 </div>
 
 
 <!-- !split -->
 <h2 id="lab-sessions-on-tuesday-and-wednesday" class="anchor">Lab sessions on Tuesday and Wednesday </h2>
 
-<ol>
-<li> Getting started with coding neural network. The exercises this week aim at setting up the feed-forward part of a neural network.</li>
-</ol>
+<p>Aim: Getting started with coding neural network. The exercises this
+week aim at setting up the feed-forward part of a neural network.
+</p>
+
 <!-- !split -->
 <h2 id="lecture-monday-october-6" class="anchor">Lecture Monday  October 6 </h2>
 

doc/pub/week41/html/week41-reveal.html

@@ -200,27 +200,26 @@ <h2 id="material-for-the-lecture-on-monday-october-6-2025">Material for the lect
 <ol>
 <p><li> Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.</li>
 <p><li> Building our own Feed-forward Neural Network, getting started
-<!-- * Video of lecture notes at <a href="https://youtu.be/pMRUbf9E-gM" target="_blank"><tt>https://youtu.be/pMRUbf9E-gM</tt></a> -->
-<!-- * Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf</tt></a> --></li>
+<!-- * Video of lecture notes at URL:"" -->
+<!-- * Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek41.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek41.pdf</tt></a> --></li>
 </ol>
-<p>
+</section>
+
+<section>
+<h2 id="readings-and-videos">Readings and Videos: </h2>
 <div class="alert alert-block alert-block alert-text-normal">
-<b>Readings and Videos:</b>
+<b></b>
 <p>
 <ol>
 <p><li> These lecture notes</li>
 <p><li> For neural networks we recommend Goodfellow et al chapters 6 and 7.</li>
-<p><li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_blank">GitHub</a>
-<ol type="a"></li>
+<p><li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_blank">GitHub</a></li>
 <p><li> Neural Networks demystified at <a href="https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs" target="_blank"><tt>https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs</tt></a></li>
-</ol>
-<p>
 <p><li> Building Neural Networks from scratch at <a href="https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex" target="_blank"><tt>https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex</tt></a></li>
 <p><li> Video on Neural Networks at <a href="https://www.youtube.com/watch?v=CqOfi41LfDw" target="_blank"><tt>https://www.youtube.com/watch?v=CqOfi41LfDw</tt></a></li>
 <p><li> Video on the back propagation algorithm at <a href="https://www.youtube.com/watch?v=Ilg3gGewQ5U" target="_blank"><tt>https://www.youtube.com/watch?v=Ilg3gGewQ5U</tt></a></li>
+<p><li> We also  recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at <a href="http://neuralnetworksanddeeplearning.com/chap4.html" target="_blank"><tt>http://neuralnetworksanddeeplearning.com/chap4.html</tt></a>.</li>
 </ol>
-<p>
-<p>We also  recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at <a href="http://neuralnetworksanddeeplearning.com/chap4.html" target="_blank"><tt>http://neuralnetworksanddeeplearning.com/chap4.html</tt></a>.</p>
 </div>
 </section>
 
@@ -242,18 +241,16 @@ <h2 id="reminder-on-books-with-hands-on-material-and-codes">Reminder on books wi
 <div class="alert alert-block alert-block alert-text-normal">
 <b></b>
 <p>
-<ul>
-<p><li> <a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank">Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch</a></li>
-</ul>
+<a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank">Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch</a>
 </div>
 </section>
 
 <section>
 <h2 id="lab-sessions-on-tuesday-and-wednesday">Lab sessions on Tuesday and Wednesday </h2>
 
-<ol>
-<p><li> Getting started with coding neural network. The exercises this week aim at setting up the feed-forward part of a neural network.</li>
-</ol>
+<p>Aim: Getting started with coding neural network. The exercises this
+week aim at setting up the feed-forward part of a neural network.
+</p>
 </section>
 
 <section>

doc/pub/week41/html/week41-solarized.html

@@ -71,6 +71,7 @@
                2,
                None,
                'material-for-the-lecture-on-monday-october-6-2025'),
+              ('Readings and Videos:', 2, None, 'readings-and-videos'),
               ('Mathematics of deep learning',
                2,
                None,
@@ -367,24 +368,24 @@ <h2 id="material-for-the-lecture-on-monday-october-6-2025">Material for the lect
 <ol>
 <li> Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.</li>
 <li> Building our own Feed-forward Neural Network, getting started
-<!-- * Video of lecture notes at <a href="https://youtu.be/pMRUbf9E-gM" target="_blank"><tt>https://youtu.be/pMRUbf9E-gM</tt></a> -->
-<!-- * Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf</tt></a> --></li>
+<!-- * Video of lecture notes at URL:"" -->
+<!-- * Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek41.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek41.pdf</tt></a> --></li>
 </ol>
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="readings-and-videos">Readings and Videos: </h2>
 <div class="alert alert-block alert-block alert-text-normal">
-<b>Readings and Videos:</b>
+<b></b>
 <p>
 <ol>
 <li> These lecture notes</li>
 <li> For neural networks we recommend Goodfellow et al chapters 6 and 7.</li>
-<li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_blank">GitHub</a>
-<ol type="a"></li>
+<li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_blank">GitHub</a></li>
 <li> Neural Networks demystified at <a href="https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs" target="_blank"><tt>https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs</tt></a></li>
-</ol>
 <li> Building Neural Networks from scratch at <a href="https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex" target="_blank"><tt>https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex</tt></a></li>
 <li> Video on Neural Networks at <a href="https://www.youtube.com/watch?v=CqOfi41LfDw" target="_blank"><tt>https://www.youtube.com/watch?v=CqOfi41LfDw</tt></a></li>
 <li> Video on the back propagation algorithm at <a href="https://www.youtube.com/watch?v=Ilg3gGewQ5U" target="_blank"><tt>https://www.youtube.com/watch?v=Ilg3gGewQ5U</tt></a></li>
+<li> We also  recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at <a href="http://neuralnetworksanddeeplearning.com/chap4.html" target="_blank"><tt>http://neuralnetworksanddeeplearning.com/chap4.html</tt></a>.</li>
 </ol>
-<p>We also  recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at <a href="http://neuralnetworksanddeeplearning.com/chap4.html" target="_blank"><tt>http://neuralnetworksanddeeplearning.com/chap4.html</tt></a>.</p>
 </div>
 
 
@@ -406,18 +407,17 @@ <h2 id="reminder-on-books-with-hands-on-material-and-codes">Reminder on books wi
 <div class="alert alert-block alert-block alert-text-normal">
 <b></b>
 <p>
-<ul>
-<li> <a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank">Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch</a></li>
-</ul>
+<a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank">Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch</a>
 </div>
 
 
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="lab-sessions-on-tuesday-and-wednesday">Lab sessions on Tuesday and Wednesday </h2>
 
-<ol>
-<li> Getting started with coding neural network. The exercises this week aim at setting up the feed-forward part of a neural network.</li>
-</ol>
+<p>Aim: Getting started with coding neural network. The exercises this
+week aim at setting up the feed-forward part of a neural network.
+</p>
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="lecture-monday-october-6">Lecture Monday  October 6 </h2>
 

doc/pub/week41/html/week41.html

@@ -148,6 +148,7 @@
                2,
                None,
                'material-for-the-lecture-on-monday-october-6-2025'),
+              ('Readings and Videos:', 2, None, 'readings-and-videos'),
               ('Mathematics of deep learning',
                2,
                None,
@@ -444,24 +445,24 @@ <h2 id="material-for-the-lecture-on-monday-october-6-2025">Material for the lect
 <ol>
 <li> Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.</li>
 <li> Building our own Feed-forward Neural Network, getting started
-<!-- * Video of lecture notes at <a href="https://youtu.be/pMRUbf9E-gM" target="_blank"><tt>https://youtu.be/pMRUbf9E-gM</tt></a> -->
-<!-- * Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf</tt></a> --></li>
+<!-- * Video of lecture notes at URL:"" -->
+<!-- * Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek41.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek41.pdf</tt></a> --></li>
 </ol>
+<!-- !split --><br><br><br><br><br><br><br><br><br><br>
+<h2 id="readings-and-videos">Readings and Videos: </h2>
 <div class="alert alert-block alert-block alert-text-normal">
-<b>Readings and Videos:</b>
+<b></b>
 <p>
 <ol>
 <li> These lecture notes</li>
 <li> For neural networks we recommend Goodfellow et al chapters 6 and 7.</li>
-<li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_blank">GitHub</a>
-<ol type="a"></li>
+<li> Rashkca et al., chapter 11, jupyter-notebook sent separately, from <a href="https://github.com/rasbt/machine-learning-book" target="_blank">GitHub</a></li>
 <li> Neural Networks demystified at <a href="https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs" target="_blank"><tt>https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs</tt></a></li>
-</ol>
 <li> Building Neural Networks from scratch at <a href="https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex" target="_blank"><tt>https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex</tt></a></li>
 <li> Video on Neural Networks at <a href="https://www.youtube.com/watch?v=CqOfi41LfDw" target="_blank"><tt>https://www.youtube.com/watch?v=CqOfi41LfDw</tt></a></li>
 <li> Video on the back propagation algorithm at <a href="https://www.youtube.com/watch?v=Ilg3gGewQ5U" target="_blank"><tt>https://www.youtube.com/watch?v=Ilg3gGewQ5U</tt></a></li>
+<li> We also  recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at <a href="http://neuralnetworksanddeeplearning.com/chap4.html" target="_blank"><tt>http://neuralnetworksanddeeplearning.com/chap4.html</tt></a>.</li>
 </ol>
-<p>We also  recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at <a href="http://neuralnetworksanddeeplearning.com/chap4.html" target="_blank"><tt>http://neuralnetworksanddeeplearning.com/chap4.html</tt></a>.</p>
 </div>
 
 
@@ -483,18 +484,17 @@ <h2 id="reminder-on-books-with-hands-on-material-and-codes">Reminder on books wi
 <div class="alert alert-block alert-block alert-text-normal">
 <b></b>
 <p>
-<ul>
-<li> <a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank">Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch</a></li>
-</ul>
+<a href="https://sebastianraschka.com/blog/2022/ml-pytorch-book.html" target="_blank">Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch</a>
 </div>
 
 
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="lab-sessions-on-tuesday-and-wednesday">Lab sessions on Tuesday and Wednesday </h2>
 
-<ol>
-<li> Getting started with coding neural network. The exercises this week aim at setting up the feed-forward part of a neural network.</li>
-</ol>
+<p>Aim: Getting started with coding neural network. The exercises this
+week aim at setting up the feed-forward part of a neural network.
+</p>
+
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="lecture-monday-october-6">Lecture Monday  October 6 </h2>