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‎doc/pub/week40/html/._week40-bs001.html‎

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@@ -257,10 +257,11 @@ <h2 id="lecture-monday-september-30-2024" class="anchor">Lecture Monday Septembe
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<div class="panel-body">
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<!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
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<ol>
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<li> Stochastic Gradient descent with examples and automatic differentiation</li>
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<li> If we get time, we start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model</li>
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<li> <a href="https://youtu.be/jdJoOrCIdII" target="_self">Video of lecture</a></li>
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<li> 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>
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<li> Logistic regression and gradient descent, examples on how to code</li>
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<li> Automatic differentiation and gradient descent, examples using Logistic regression</li>
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<li> Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model
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<!-- o <a href="https://youtu.be/jdJoOrCIdII" target="_self">Video of lecture</a> -->
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<!-- 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>
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</ol>
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‎doc/pub/week40/html/._week40-bs002.html‎

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@@ -259,9 +259,9 @@ <h2 id="suggested-readings-and-videos" class="anchor">Suggested readings and vid
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<ol>
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<li> The lecture notes for week 40 (these notes)</li>
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<li> For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and sections 8.3-8.6. We will come back to the latter chapter in our discussion of Neural networks as well.</li>
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<li> For neural networks we recommend Goodfellow et al chapter 6 and Raschka et al chapter 2 (contains also material about gradient descent) and chapter 11 (we will use this next week)</li>
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<li> Video on gradient descent at <a href="https://www.youtube.com/watch?v=sDv4f4s2SB8" target="_self"><tt>https://www.youtube.com/watch?v=sDv4f4s2SB8</tt></a></li>
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<li> Video on stochastic gradient descent at <a href="https://www.youtube.com/watch?v=vMh0zPT0tLI" target="_self"><tt>https://www.youtube.com/watch?v=vMh0zPT0tLI</tt></a></li>
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<li> For neural networks we recommend Goodfellow et al chapter 6 and Raschka et al chapter 2 (contains also material about gradient descent) and chapter 11 (we will use this next week)
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<!-- o Video on gradient descent at <a href="https://www.youtube.com/watch?v=sDv4f4s2SB8" target="_self"><tt>https://www.youtube.com/watch?v=sDv4f4s2SB8</tt></a> --></li>
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<li> Video on automatic differentiation at <a href="https://www.youtube.com/watch?v=wG_nF1awSSY" target="_self"><tt>https://www.youtube.com/watch?v=wG_nF1awSSY</tt></a></li>
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<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>
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<li> Building Neural Networks from scratch at URL:https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex"</li>
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</ol>

‎doc/pub/week40/html/week40-reveal.html‎

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<b></b>
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<p>
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<ol>
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<p><li> Stochastic Gradient descent with examples and automatic differentiation</li>
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<p><li> If we get time, we start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model</li>
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<p><li> <a href="https://youtu.be/jdJoOrCIdII" target="_blank">Video of lecture</a></li>
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<p><li> 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>
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<p><li> Logistic regression and gradient descent, examples on how to code</li>
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<p><li> Automatic differentiation and gradient descent, examples using Logistic regression</li>
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<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
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<!-- o <a href="https://youtu.be/jdJoOrCIdII" target="_blank">Video of lecture</a> -->
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<!-- 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>
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</ol>
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</div>
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</section>
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<p><li> For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and sections 8.3-8.6. We will come back to the latter chapter in our discussion of Neural networks as well.</li>
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<p><li> For neural networks we recommend Goodfellow et al chapter 6 and Raschka et al chapter 2 (contains also material about gradient descent) and chapter 11 (we will use this next week)</li>
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<p><li> For neural networks we recommend Goodfellow et al chapter 6 and Raschka et al chapter 2 (contains also material about gradient descent) and chapter 11 (we will use this next week)
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<!-- o Video on gradient descent at <a href="https://www.youtube.com/watch?v=sDv4f4s2SB8" target="_blank"><tt>https://www.youtube.com/watch?v=sDv4f4s2SB8</tt></a> --></li>
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<p><li> Video on gradient descent at <a href="https://www.youtube.com/watch?v=sDv4f4s2SB8" target="_blank"><tt>https://www.youtube.com/watch?v=sDv4f4s2SB8</tt></a></li>
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<p><li> Video on stochastic gradient descent at <a href="https://www.youtube.com/watch?v=vMh0zPT0tLI" target="_blank"><tt>https://www.youtube.com/watch?v=vMh0zPT0tLI</tt></a></li>
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<p><li> Video on automatic differentiation at <a href="https://www.youtube.com/watch?v=wG_nF1awSSY" target="_blank"><tt>https://www.youtube.com/watch?v=wG_nF1awSSY</tt></a></li>
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<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>
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‎doc/pub/week40/html/week40-solarized.html‎

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<b></b>
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<p>
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<ol>
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<li> Stochastic Gradient descent with examples and automatic differentiation</li>
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<li> If we get time, we start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model</li>
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<li> <a href="https://youtu.be/jdJoOrCIdII" target="_blank">Video of lecture</a></li>
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<li> 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>
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<li> Logistic regression and gradient descent, examples on how to code</li>
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<li> Automatic differentiation and gradient descent, examples using Logistic regression</li>
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<li> Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model
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<!-- o <a href="https://youtu.be/jdJoOrCIdII" target="_blank">Video of lecture</a> -->
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<!-- 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>
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</ol>
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</div>
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<ol>
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<li> The lecture notes for week 40 (these notes)</li>
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<li> For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and sections 8.3-8.6. We will come back to the latter chapter in our discussion of Neural networks as well.</li>
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<li> For neural networks we recommend Goodfellow et al chapter 6 and Raschka et al chapter 2 (contains also material about gradient descent) and chapter 11 (we will use this next week)</li>
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<li> Video on gradient descent at <a href="https://www.youtube.com/watch?v=sDv4f4s2SB8" target="_blank"><tt>https://www.youtube.com/watch?v=sDv4f4s2SB8</tt></a></li>
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<li> Video on stochastic gradient descent at <a href="https://www.youtube.com/watch?v=vMh0zPT0tLI" target="_blank"><tt>https://www.youtube.com/watch?v=vMh0zPT0tLI</tt></a></li>
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<li> For neural networks we recommend Goodfellow et al chapter 6 and Raschka et al chapter 2 (contains also material about gradient descent) and chapter 11 (we will use this next week)
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<!-- o Video on gradient descent at <a href="https://www.youtube.com/watch?v=sDv4f4s2SB8" target="_blank"><tt>https://www.youtube.com/watch?v=sDv4f4s2SB8</tt></a> --></li>
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<li> Video on automatic differentiation at <a href="https://www.youtube.com/watch?v=wG_nF1awSSY" target="_blank"><tt>https://www.youtube.com/watch?v=wG_nF1awSSY</tt></a></li>
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<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>
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<li> Building Neural Networks from scratch at URL:https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex"</li>
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</ol>

‎doc/pub/week40/html/week40.html‎

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<b></b>
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<p>
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<ol>
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<li> Stochastic Gradient descent with examples and automatic differentiation</li>
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<li> If we get time, we start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model</li>
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<li> <a href="https://youtu.be/jdJoOrCIdII" target="_blank">Video of lecture</a></li>
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<li> 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>
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<li> Logistic regression and gradient descent, examples on how to code</li>
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<li> Automatic differentiation and gradient descent, examples using Logistic regression</li>
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<li> Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model
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<!-- o <a href="https://youtu.be/jdJoOrCIdII" target="_blank">Video of lecture</a> -->
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<!-- 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>
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</ol>
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</div>
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<ol>
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<li> The lecture notes for week 40 (these notes)</li>
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<li> For a good discussion on gradient methods, we would like to recommend Goodfellow et al section 4.3-4.5 and sections 8.3-8.6. We will come back to the latter chapter in our discussion of Neural networks as well.</li>
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<li> For neural networks we recommend Goodfellow et al chapter 6 and Raschka et al chapter 2 (contains also material about gradient descent) and chapter 11 (we will use this next week)</li>
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<li> Video on gradient descent at <a href="https://www.youtube.com/watch?v=sDv4f4s2SB8" target="_blank"><tt>https://www.youtube.com/watch?v=sDv4f4s2SB8</tt></a></li>
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<li> Video on stochastic gradient descent at <a href="https://www.youtube.com/watch?v=vMh0zPT0tLI" target="_blank"><tt>https://www.youtube.com/watch?v=vMh0zPT0tLI</tt></a></li>
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<li> For neural networks we recommend Goodfellow et al chapter 6 and Raschka et al chapter 2 (contains also material about gradient descent) and chapter 11 (we will use this next week)
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<!-- o Video on gradient descent at <a href="https://www.youtube.com/watch?v=sDv4f4s2SB8" target="_blank"><tt>https://www.youtube.com/watch?v=sDv4f4s2SB8</tt></a> --></li>
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<li> Video on automatic differentiation at <a href="https://www.youtube.com/watch?v=wG_nF1awSSY" target="_blank"><tt>https://www.youtube.com/watch?v=wG_nF1awSSY</tt></a></li>
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<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>
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<li> Building Neural Networks from scratch at URL:https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex"</li>
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‎doc/pub/week40/ipynb/ipynb-week40-src.tar.gz‎

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