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update week 40

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Morten Hjorth-Jensen
2025-09-28 10:58:16 +02:00
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commit e7ec5ea971
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doc/pub/week40/html/._week40-bs001.html
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@@ -257,10 +257,11 @@ MathJax.Hub.Config({
<div class="panel-body"> <div class="panel-body">
<!-- subsequent paragraphs come in larger fonts, so start with a paragraph --> <!-- subsequent paragraphs come in larger fonts, so start with a paragraph -->
<ol> <ol>
<li> Stochastic Gradient descent with examples and automatic differentiation</li> <li> Logistic regression and gradient descent, examples on how to code</li>
<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> <li> Automatic differentiation and gradient descent, examples using Logistic regression</li>
<li> <a href="https://youtu.be/jdJoOrCIdII" target="_self">Video of lecture</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> 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> <!-- 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>
</ol> </ol>
</div> </div>
</div> </div>
doc/pub/week40/html/._week40-bs002.html
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@@ -259,9 +259,9 @@ MathJax.Hub.Config({
<ol> <ol>
<li> The lecture notes for week 40 (these notes)</li> <li> The lecture notes for week 40 (these notes)</li>
<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> <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>
<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> <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> 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> <!-- 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>
<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> <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>
<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> <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>
<li> Building Neural Networks from scratch at URL:https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex"</li> <li> Building Neural Networks from scratch at URL:https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex"</li>
</ol> </ol>
doc/pub/week40/html/week40-reveal.html
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@@ -197,10 +197,11 @@ MathJax.Hub.Config({
<b></b> <b></b>
<p> <p>
<ol> <ol>
<p><li> Stochastic Gradient descent with examples and automatic differentiation</li> <p><li> Logistic regression and gradient descent, examples on how to code</li>
<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> <p><li> Automatic differentiation and gradient descent, examples using Logistic regression</li>
<p><li> <a href="https://youtu.be/jdJoOrCIdII" target="_blank">Video of lecture</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
<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> <!-- 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>
</ol> </ol>
</div> </div>
</section> </section>
@@ -216,11 +217,10 @@ MathJax.Hub.Config({
<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> <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>
<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> <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)
<!-- 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>
<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> <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>
<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>
<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> <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>
doc/pub/week40/html/week40-solarized.html
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@@ -232,10 +232,11 @@ MathJax.Hub.Config({
<b></b> <b></b>
<p> <p>
<ol> <ol>
<li> Stochastic Gradient descent with examples and automatic differentiation</li> <li> Logistic regression and gradient descent, examples on how to code</li>
<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> <li> Automatic differentiation and gradient descent, examples using Logistic regression</li>
<li> <a href="https://youtu.be/jdJoOrCIdII" target="_blank">Video of lecture</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> 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> <!-- 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>
</ol> </ol>
</div> </div>
@@ -248,9 +249,9 @@ MathJax.Hub.Config({
<ol> <ol>
<li> The lecture notes for week 40 (these notes)</li> <li> The lecture notes for week 40 (these notes)</li>
<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> <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>
<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> <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> 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> <!-- 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>
<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> <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>
<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> <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>
<li> Building Neural Networks from scratch at URL:https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex"</li> <li> Building Neural Networks from scratch at URL:https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex"</li>
</ol> </ol>
doc/pub/week40/html/week40.html
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@@ -309,10 +309,11 @@ MathJax.Hub.Config({
<b></b> <b></b>
<p> <p>
<ol> <ol>
<li> Stochastic Gradient descent with examples and automatic differentiation</li> <li> Logistic regression and gradient descent, examples on how to code</li>
<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> <li> Automatic differentiation and gradient descent, examples using Logistic regression</li>
<li> <a href="https://youtu.be/jdJoOrCIdII" target="_blank">Video of lecture</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> 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> <!-- 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>
</ol> </ol>
</div> </div>
@@ -325,9 +326,9 @@ MathJax.Hub.Config({
<ol> <ol>
<li> The lecture notes for week 40 (these notes)</li> <li> The lecture notes for week 40 (these notes)</li>
<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> <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>
<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> <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> 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> <!-- 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>
<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> <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>
<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> <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>
<li> Building Neural Networks from scratch at URL:https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex"</li> <li> Building Neural Networks from scratch at URL:https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex"</li>
</ol> </ol>
doc/pub/week40/ipynb/ipynb-week40-src.tar.gz
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doc/pub/week40/ipynb/week40.ipynb
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"source": [ "source": [
"## Lecture Monday September 30, 2024\n", "## Lecture Monday September 30, 2024\n",
"1. Stochastic Gradient descent with examples and automatic differentiation\n", "1. Logistic regression and gradient descent, examples on how to code\n",
"\n", "\n",
"2. 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\n", "2. Automatic differentiation and gradient descent, examples using Logistic regression\n",
"\n", "\n",
"3. [Video of lecture](https://youtu.be/jdJoOrCIdII)\n", "3. Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model\n",
"\n", "<!-- o [Video of lecture](https://youtu.be/jdJoOrCIdII) -->\n",
"4. Whiteboard notes at <https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesSeptember30.pdf>" "<!-- o Whiteboard notes at <https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesSeptember30.pdf> -->"
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@@ -57,19 +57,18 @@
"2. 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.\n", "2. 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.\n",
"\n", "\n",
"3. 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)\n", "3. 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)\n",
"<!-- o Video on gradient descent at <https://www.youtube.com/watch?v=sDv4f4s2SB8> -->\n",
"\n", "\n",
"4. Video on gradient descent at <https://www.youtube.com/watch?v=sDv4f4s2SB8>\n", "4. Video on automatic differentiation at <https://www.youtube.com/watch?v=wG_nF1awSSY>\n",
"\n", "\n",
"5. Video on stochastic gradient descent at <https://www.youtube.com/watch?v=vMh0zPT0tLI>\n", "5. Neural Networks demystified at <https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs>\n",
"\n", "\n",
"6. Neural Networks demystified at <https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs>\n", "6. Building Neural Networks from scratch at URL:https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex\""
"\n",
"7. Building Neural Networks from scratch at URL:https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex\""
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@@ -2282,7 +2281,7 @@
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@@ -2294,7 +2293,7 @@
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@@ -2304,7 +2303,7 @@
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@@ -2316,7 +2315,7 @@
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@@ -2333,7 +2332,7 @@
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doc/src/week40/week40.do.txt
+3 -3
View File
@@ -8,7 +8,7 @@ DATE: September 29-October 3, 2025
===== Lecture Monday September 30, 2024 ===== ===== Lecture Monday September 30, 2024 =====
!bblock !bblock
o Logistic regression and gradient descent, examples on how to code o Logistic regression and gradient descent, examples on how to code
o Stochastic Gradient descent and automatic differentiation, examples using Logistic regression 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 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 "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 Whiteboard notes at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesSeptember30.pdf"
@@ -20,8 +20,8 @@ o Start with the basics of Neural Networks, setting up the basic steps, from the
o The lecture notes for week 40 (these notes) o The lecture notes for week 40 (these notes)
o 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. o 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.
o 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) o 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)
o Video on gradient descent at URL:"https://www.youtube.com/watch?v=sDv4f4s2SB8" # o Video on gradient descent at URL:"https://www.youtube.com/watch?v=sDv4f4s2SB8"
o Video on stochastic gradient descent at URL:"https://www.youtube.com/watch?v=vMh0zPT0tLI" o Video on automatic differentiation at URL:"https://www.youtube.com/watch?v=wG_nF1awSSY"
o Neural Networks demystified at URL:"https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs" o Neural Networks demystified at URL:"https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs"
o Building Neural Networks from scratch at URL:https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex" o Building Neural Networks from scratch at URL:https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex"
!eblock !eblock