update week 43

Author: mhjensen

doc/pub/week43/html/week43-bs.html

@@ -396,13 +396,14 @@ <h2 id="plans-for-week-43" class="anchor">Plans for week 43 </h2>
 <div class="panel panel-default">
 <div class="panel-body">
 <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
-<ul>
-  <li> Building our own Feed-forward Neural Network with intro to Tensorflow</li>
-  <li> Solving differential equations with Neural Networks
+<ol>
+<li> Reminder from last week, see lalso ecture notes from week 42 at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week42.html" target="_self"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week42.html</tt></a> as well as those from week 41, see see <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html" target="_self"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html</tt></a>.</li> 
+<li> Building our own Feed-forward Neural Network.</li>
+<li> Coding examples using Tensorflow/Keras and Pytorch examples. The Pytorch examples are adapted from Rashcka's text, see chapters 11-13..</li> 
+<li> Start discussions on how to use neural networks for solving  differential equations (ordinary and partial ones). This topic continues next week as well.
 <!-- * Video of lecture at <a href="https://youtu.be/vkBNTn-MLqs" target="_self"><tt>https://youtu.be/vkBNTn-MLqs</tt></a> -->
-<!-- * Video os second part, solving differential equations with neural networks at <a href="https://youtu.be/2N8To65I2wQ" target="_self"><tt>https://youtu.be/2N8To65I2wQ</tt></a> -->
-<!-- * Whiteboard notes on solving differential equations at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf" target="_self"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf</tt></a> --></li>
-</ul>
+<!-- * Whiteboard notes on solving differential equations at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf" target="_self"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf</tt></a> --></li>
+</ol>
 </div>
 </div>
 
@@ -412,11 +413,10 @@ <h2 id="exercises-and-lab-session-week-43" class="anchor">Exercises and lab sess
 <div class="panel panel-default">
 <div class="panel-body">
 <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
-<ul>
-  <li> Exercise on writing your own neural network code</li>
-  <li> The exercises this week will be continued next week as well</li>
-  <li> Discussion of project 2</li>
-</ul>
+<ol>
+<li> Work  on writing your own neural network code and discussions of project 2. If you didn't get time to do the exercises from the two last weeks, we recommend doing so as these exercises give you the basic elements of a neural network code.</li>
+<li> The exercises this week are tailored to the optional part of project 2, and deal with studying ways to display results from classification problems</li>
+</ol>
 </div>
 </div>
 
@@ -425,7 +425,7 @@ <h2 id="exercises-and-lab-session-week-43" class="anchor">Exercises and lab sess
 <h2 id="using-automatic-differentiation" class="anchor">Using Automatic differentiation </h2>
 
 <p>In our discussions of ordinary differential equations and neural network codes
-we will also study the usage of Autograd, see for example <a href="https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola" target="_self"><tt>https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola</tt></a> in computing gradients for deep learning. For the documentation of Autograd and examples see the Autograd documentation at <a href="https://github.com/HIPS/autograd" target="_self"><tt>https://github.com/HIPS/autograd</tt></a> and the lecture slides from week 40, see <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html" target="_self"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html</tt></a>. 
+we will also study the usage of Autograd, see for example <a href="https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola" target="_self"><tt>https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola</tt></a> in computing gradients for deep learning. For the documentation of Autograd and examples see the Autograd documentation at <a href="https://github.com/HIPS/autograd" target="_self"><tt>https://github.com/HIPS/autograd</tt></a> and the lecture slides from week 41, see <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html" target="_self"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html</tt></a>. 
 </p>
 
 <!-- !split -->

doc/pub/week43/html/week43-reveal.html

@@ -197,15 +197,14 @@ <h2 id="plans-for-week-43">Plans for week 43 </h2>
 <div class="alert alert-block alert-block alert-text-normal">
 <b>Material for the lecture on Monday October 20, 2025</b>
 <p>
-<ul>
-
-<p><li> Building our own Feed-forward Neural Network with intro to Tensorflow</li>
-
-<p><li> Solving differential equations with Neural Networks
+<ol>
+<p><li> Reminder from last week, see lalso ecture notes from week 42 at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week42.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week42.html</tt></a> as well as those from week 41, see see <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html</tt></a>.</li> 
+<p><li> Building our own Feed-forward Neural Network.</li>
+<p><li> Coding examples using Tensorflow/Keras and Pytorch examples. The Pytorch examples are adapted from Rashcka's text, see chapters 11-13..</li> 
+<p><li> Start discussions on how to use neural networks for solving  differential equations (ordinary and partial ones). This topic continues next week as well.
 <!-- * Video of lecture at <a href="https://youtu.be/vkBNTn-MLqs" target="_blank"><tt>https://youtu.be/vkBNTn-MLqs</tt></a> -->
-<!-- * Video os second part, solving differential equations with neural networks at <a href="https://youtu.be/2N8To65I2wQ" target="_blank"><tt>https://youtu.be/2N8To65I2wQ</tt></a> -->
-<!-- * Whiteboard notes on solving differential equations at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf</tt></a> --></li>
-</ul>
+<!-- * Whiteboard notes on solving differential equations at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf</tt></a> --></li>
+</ol>
 </div>
 </section>
 
@@ -214,22 +213,18 @@ <h2 id="exercises-and-lab-session-week-43">Exercises and lab session week 43 </h
 <div class="alert alert-block alert-block alert-text-normal">
 <b>Lab sessions on Tuesday and Wednesday</b>
 <p>
-<ul>
-
-<p><li> Exercise on writing your own neural network code</li>
-
-<p><li> The exercises this week will be continued next week as well</li>
-
-<p><li> Discussion of project 2</li>
-</ul>
+<ol>
+<p><li> Work  on writing your own neural network code and discussions of project 2. If you didn't get time to do the exercises from the two last weeks, we recommend doing so as these exercises give you the basic elements of a neural network code.</li>
+<p><li> The exercises this week are tailored to the optional part of project 2, and deal with studying ways to display results from classification problems</li>
+</ol>
 </div>
 </section>
 
 <section>
 <h2 id="using-automatic-differentiation">Using Automatic differentiation </h2>
 
 <p>In our discussions of ordinary differential equations and neural network codes
-we will also study the usage of Autograd, see for example <a href="https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola" target="_blank"><tt>https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola</tt></a> in computing gradients for deep learning. For the documentation of Autograd and examples see the Autograd documentation at <a href="https://github.com/HIPS/autograd" target="_blank"><tt>https://github.com/HIPS/autograd</tt></a> and the lecture slides from week 40, see <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html</tt></a>. 
+we will also study the usage of Autograd, see for example <a href="https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola" target="_blank"><tt>https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola</tt></a> in computing gradients for deep learning. For the documentation of Autograd and examples see the Autograd documentation at <a href="https://github.com/HIPS/autograd" target="_blank"><tt>https://github.com/HIPS/autograd</tt></a> and the lecture slides from week 41, see <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html</tt></a>. 
 </p>
 </section>
 

doc/pub/week43/html/week43-solarized.html

@@ -317,13 +317,14 @@ <h2 id="plans-for-week-43">Plans for week 43 </h2>
 <div class="alert alert-block alert-block alert-text-normal">
 <b>Material for the lecture on Monday October 20, 2025</b>
 <p>
-<ul>
-  <li> Building our own Feed-forward Neural Network with intro to Tensorflow</li>
-  <li> Solving differential equations with Neural Networks
+<ol>
+<li> Reminder from last week, see lalso ecture notes from week 42 at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week42.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week42.html</tt></a> as well as those from week 41, see see <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html</tt></a>.</li> 
+<li> Building our own Feed-forward Neural Network.</li>
+<li> Coding examples using Tensorflow/Keras and Pytorch examples. The Pytorch examples are adapted from Rashcka's text, see chapters 11-13..</li> 
+<li> Start discussions on how to use neural networks for solving  differential equations (ordinary and partial ones). This topic continues next week as well.
 <!-- * Video of lecture at <a href="https://youtu.be/vkBNTn-MLqs" target="_blank"><tt>https://youtu.be/vkBNTn-MLqs</tt></a> -->
-<!-- * Video os second part, solving differential equations with neural networks at <a href="https://youtu.be/2N8To65I2wQ" target="_blank"><tt>https://youtu.be/2N8To65I2wQ</tt></a> -->
-<!-- * Whiteboard notes on solving differential equations at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf</tt></a> --></li>
-</ul>
+<!-- * Whiteboard notes on solving differential equations at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf</tt></a> --></li>
+</ol>
 </div>
 
 
@@ -332,19 +333,18 @@ <h2 id="exercises-and-lab-session-week-43">Exercises and lab session week 43 </h
 <div class="alert alert-block alert-block alert-text-normal">
 <b>Lab sessions on Tuesday and Wednesday</b>
 <p>
-<ul>
-  <li> Exercise on writing your own neural network code</li>
-  <li> The exercises this week will be continued next week as well</li>
-  <li> Discussion of project 2</li>
-</ul>
+<ol>
+<li> Work  on writing your own neural network code and discussions of project 2. If you didn't get time to do the exercises from the two last weeks, we recommend doing so as these exercises give you the basic elements of a neural network code.</li>
+<li> The exercises this week are tailored to the optional part of project 2, and deal with studying ways to display results from classification problems</li>
+</ol>
 </div>
 
 
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="using-automatic-differentiation">Using Automatic differentiation </h2>
 
 <p>In our discussions of ordinary differential equations and neural network codes
-we will also study the usage of Autograd, see for example <a href="https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola" target="_blank"><tt>https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola</tt></a> in computing gradients for deep learning. For the documentation of Autograd and examples see the Autograd documentation at <a href="https://github.com/HIPS/autograd" target="_blank"><tt>https://github.com/HIPS/autograd</tt></a> and the lecture slides from week 40, see <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html</tt></a>. 
+we will also study the usage of Autograd, see for example <a href="https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola" target="_blank"><tt>https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola</tt></a> in computing gradients for deep learning. For the documentation of Autograd and examples see the Autograd documentation at <a href="https://github.com/HIPS/autograd" target="_blank"><tt>https://github.com/HIPS/autograd</tt></a> and the lecture slides from week 41, see <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html</tt></a>. 
 </p>
 
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>

doc/pub/week43/html/week43.html

@@ -394,13 +394,14 @@ <h2 id="plans-for-week-43">Plans for week 43 </h2>
 <div class="alert alert-block alert-block alert-text-normal">
 <b>Material for the lecture on Monday October 20, 2025</b>
 <p>
-<ul>
-  <li> Building our own Feed-forward Neural Network with intro to Tensorflow</li>
-  <li> Solving differential equations with Neural Networks
+<ol>
+<li> Reminder from last week, see lalso ecture notes from week 42 at <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week42.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week42.html</tt></a> as well as those from week 41, see see <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html</tt></a>.</li> 
+<li> Building our own Feed-forward Neural Network.</li>
+<li> Coding examples using Tensorflow/Keras and Pytorch examples. The Pytorch examples are adapted from Rashcka's text, see chapters 11-13..</li> 
+<li> Start discussions on how to use neural networks for solving  differential equations (ordinary and partial ones). This topic continues next week as well.
 <!-- * Video of lecture at <a href="https://youtu.be/vkBNTn-MLqs" target="_blank"><tt>https://youtu.be/vkBNTn-MLqs</tt></a> -->
-<!-- * Video os second part, solving differential equations with neural networks at <a href="https://youtu.be/2N8To65I2wQ" target="_blank"><tt>https://youtu.be/2N8To65I2wQ</tt></a> -->
-<!-- * Whiteboard notes on solving differential equations at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOct21.pdf</tt></a> --></li>
-</ul>
+<!-- * Whiteboard notes on solving differential equations at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek43.pdf</tt></a> --></li>
+</ol>
 </div>
 
 
@@ -409,19 +410,18 @@ <h2 id="exercises-and-lab-session-week-43">Exercises and lab session week 43 </h
 <div class="alert alert-block alert-block alert-text-normal">
 <b>Lab sessions on Tuesday and Wednesday</b>
 <p>
-<ul>
-  <li> Exercise on writing your own neural network code</li>
-  <li> The exercises this week will be continued next week as well</li>
-  <li> Discussion of project 2</li>
-</ul>
+<ol>
+<li> Work  on writing your own neural network code and discussions of project 2. If you didn't get time to do the exercises from the two last weeks, we recommend doing so as these exercises give you the basic elements of a neural network code.</li>
+<li> The exercises this week are tailored to the optional part of project 2, and deal with studying ways to display results from classification problems</li>
+</ol>
 </div>
 
 
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>
 <h2 id="using-automatic-differentiation">Using Automatic differentiation </h2>
 
 <p>In our discussions of ordinary differential equations and neural network codes
-we will also study the usage of Autograd, see for example <a href="https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola" target="_blank"><tt>https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola</tt></a> in computing gradients for deep learning. For the documentation of Autograd and examples see the Autograd documentation at <a href="https://github.com/HIPS/autograd" target="_blank"><tt>https://github.com/HIPS/autograd</tt></a> and the lecture slides from week 40, see <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html</tt></a>. 
+we will also study the usage of Autograd, see for example <a href="https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola" target="_blank"><tt>https://www.youtube.com/watch?v=fRf4l5qaX1M&ab_channel=AlexSmola</tt></a> in computing gradients for deep learning. For the documentation of Autograd and examples see the Autograd documentation at <a href="https://github.com/HIPS/autograd" target="_blank"><tt>https://github.com/HIPS/autograd</tt></a> and the lecture slides from week 41, see <a href="https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html" target="_blank"><tt>https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/week41.html</tt></a>. 
 </p>
 
 <!-- !split --><br><br><br><br><br><br><br><br><br><br>