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updating notes with links

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Morten Hjorth-Jensen
2025-09-29 16:23:16 +02:00
parent 6c5f9576c3
commit 932bef9f25
14 changed files with 586 additions and 876 deletions
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doc/LectureNotes/_build/.doctrees/environment.pickle
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@@ -465,9 +465,10 @@ doconce format html week40.do.txt --no_mako -->
<!-- o Automatic differentiation and gradient descent, examples using Logistic regression -->
<ol class="arabic simple" start="2">
<li><p>Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model</p></li>
<li><p>Video of lecture at <a class="reference external" href="https://youtu.be/MS3Tv8FVArs">https://youtu.be/MS3Tv8FVArs</a></p></li>
<li><p>Whiteboard notes at <a class="github reference external" href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek40.pdf">CompPhysics/MachineLearning</a></p></li>
</ol>
<!-- o [Video of lecture](https://youtu.be/jdJoOrCIdII) -->
<!-- o Whiteboard notes at <https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesSeptember30.pdf> --></section>
</section>
<section id="suggested-readings-and-videos">
<h2>Suggested readings and videos<a class="headerlink" href="#suggested-readings-and-videos" title="Link to this heading">#</a></h2>
<p><strong>Readings and Videos:</strong></p>
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@@ -267,9 +267,9 @@ MathJax.Hub.Config({
<ol>
<li> Logistic regression and gradient descent, examples on how to code
<!-- o Automatic differentiation and gradient descent, examples using Logistic regression --></li>
<li> Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model
<!-- o <a href="https://youtu.be/jdJoOrCIdII" target="_self">Video of lecture</a> -->
<!-- o Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesSeptember30.pdf" target="_self"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesSeptember30.pdf</tt></a> --></li>
<li> Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model</li>
<li> Video of lecture at <a href="https://youtu.be/MS3Tv8FVArs" target="_self"><tt>https://youtu.be/MS3Tv8FVArs</tt></a></li>
<li> Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek40.pdf" target="_self"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek40.pdf</tt></a></li>
</ol>
</div>
</div>
doc/pub/week40/html/week40-reveal.html
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@@ -199,9 +199,9 @@ MathJax.Hub.Config({
<ol>
<p><li> Logistic regression and gradient descent, examples on how to code
<!-- o Automatic differentiation and gradient descent, examples using Logistic regression --></li>
<p><li> Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model
<!-- o <a href="https://youtu.be/jdJoOrCIdII" target="_blank">Video of lecture</a> -->
<!-- o Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesSeptember30.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesSeptember30.pdf</tt></a> --></li>
<p><li> Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model</li>
<p><li> Video of lecture at <a href="https://youtu.be/MS3Tv8FVArs" target="_blank"><tt>https://youtu.be/MS3Tv8FVArs</tt></a></li>
<p><li> Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek40.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek40.pdf</tt></a></li>
</ol>
</div>
</section>
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@@ -240,9 +240,9 @@ MathJax.Hub.Config({
<ol>
<li> Logistic regression and gradient descent, examples on how to code
<!-- o Automatic differentiation and gradient descent, examples using Logistic regression --></li>
<li> Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model
<!-- o <a href="https://youtu.be/jdJoOrCIdII" target="_blank">Video of lecture</a> -->
<!-- o Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesSeptember30.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesSeptember30.pdf</tt></a> --></li>
<li> Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model</li>
<li> Video of lecture at <a href="https://youtu.be/MS3Tv8FVArs" target="_blank"><tt>https://youtu.be/MS3Tv8FVArs</tt></a></li>
<li> Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek40.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek40.pdf</tt></a></li>
</ol>
</div>
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@@ -317,9 +317,9 @@ MathJax.Hub.Config({
<ol>
<li> Logistic regression and gradient descent, examples on how to code
<!-- o Automatic differentiation and gradient descent, examples using Logistic regression --></li>
<li> Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model
<!-- o <a href="https://youtu.be/jdJoOrCIdII" target="_blank">Video of lecture</a> -->
<!-- o Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesSeptember30.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesSeptember30.pdf</tt></a> --></li>
<li> Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model</li>
<li> Video of lecture at <a href="https://youtu.be/MS3Tv8FVArs" target="_blank"><tt>https://youtu.be/MS3Tv8FVArs</tt></a></li>
<li> Whiteboard notes at <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek40.pdf" target="_blank"><tt>https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek40.pdf</tt></a></li>
</ol>
</div>
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@@ -10,8 +10,8 @@ DATE: September 29-October 3, 2025
o Logistic regression and gradient descent, examples on how to code
#o Automatic differentiation and gradient descent, examples using Logistic regression
o Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model
# o "Video of lecture":"https://youtu.be/jdJoOrCIdII"
# o Whiteboard notes at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesSeptember30.pdf"
o Video of lecture at URL:"https://youtu.be/MS3Tv8FVArs"
o Whiteboard notes at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek40.pdf"
!eblock
!split