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Morten Hjorth-Jensen
2024-10-06 12:29:55 +02:00
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doc/pub/week41/html/._week41-bs002.html
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@@ -342,22 +342,30 @@ MathJax.Hub.Config({
<a name="part0002"></a>
<!-- !split -->
<h2 id="material-for-the-lecture-on-monday-october-7-2024" class="anchor">Material for the lecture on Monday October 7, 2024 </h2>
<ul>
<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
<!-- * <a href="https://youtu.be/5-RRTO9uDvI" target="_self">Video of lecture notes</a> -->
<!-- * <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf" target="_self">Whiteboard notes</a> --></li>
<li> Readings and Videos:</li>
<ul>
<li> These lecture notes</li>
<li> For neural networks we recommend Goodfellow et al chapter 6.</li>
<li> <a href="https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs" target="_self">Neural Networks demystified</a></li>
<li> <a href="https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex" target="_self">Building Neural Networks from scratch</a></li>
<li> <a href="https://www.youtube.com/watch?v=CqOfi41LfDw" target="_self">Video on Neural Networks</a></li>
<li> <a href="https://www.youtube.com/watch?v=Ilg3gGewQ5U" target="_self">Video on the back propagation algorithm</a></li>
</ul>
</ul>
<p>I 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>
</ol>
<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> Rashcka et al chapter 11</li>
<li> For neural networks we recommend Goodfellow et al chapter 6.
<ol type="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>
</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>
<p>
<!-- navigation buttons at the bottom of the page -->
doc/pub/week41/html/week41-reveal.html
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<section>
<h2 id="material-for-the-lecture-on-monday-october-7-2024">Material for the lecture on Monday October 7, 2024 </h2>
<ul>
<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
<!-- * <a href="https://youtu.be/5-RRTO9uDvI" target="_blank">Video of lecture notes</a> -->
<!-- * <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf" target="_blank">Whiteboard notes</a> --></li>
<p><li> Readings and Videos:</li>
<ul>
</ol>
<p>
<div class="alert alert-block alert-block alert-text-normal">
<b>Readings and Videos:</b>
<p>
<ol>
<p><li> These lecture notes</li>
<p><li> For neural networks we recommend Goodfellow et al chapter 6.</li>
<p><li> <a href="https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs" target="_blank">Neural Networks demystified</a></li>
<p><li> <a href="https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex" target="_blank">Building Neural Networks from scratch</a></li>
<p><li> <a href="https://www.youtube.com/watch?v=CqOfi41LfDw" target="_blank">Video on Neural Networks</a></li>
<p><li> <a href="https://www.youtube.com/watch?v=Ilg3gGewQ5U" target="_blank">Video on the back propagation algorithm</a></li>
</ul>
<p><li> Rashcka et al chapter 11</li>
<p><li> For neural networks we recommend Goodfellow et al chapter 6.
<ol type="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>
</ul>
<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>
</ol>
<p>
<p>I 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>
<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>
<section>
doc/pub/week41/html/week41-solarized.html
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@@ -298,22 +298,29 @@ MathJax.Hub.Config({
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="material-for-the-lecture-on-monday-october-7-2024">Material for the lecture on Monday October 7, 2024 </h2>
<ul>
<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
<!-- * <a href="https://youtu.be/5-RRTO9uDvI" target="_blank">Video of lecture notes</a> -->
<!-- * <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf" target="_blank">Whiteboard notes</a> --></li>
<li> Readings and Videos:</li>
<ul>
<li> These lecture notes</li>
<li> For neural networks we recommend Goodfellow et al chapter 6.</li>
<li> <a href="https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs" target="_blank">Neural Networks demystified</a></li>
<li> <a href="https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex" target="_blank">Building Neural Networks from scratch</a></li>
<li> <a href="https://www.youtube.com/watch?v=CqOfi41LfDw" target="_blank">Video on Neural Networks</a></li>
<li> <a href="https://www.youtube.com/watch?v=Ilg3gGewQ5U" target="_blank">Video on the back propagation algorithm</a></li>
</ul>
</ul>
<p>I 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>
</ol>
<div class="alert alert-block alert-block alert-text-normal">
<b>Readings and Videos:</b>
<p>
<ol>
<li> These lecture notes</li>
<li> Rashcka et al chapter 11</li>
<li> For neural networks we recommend Goodfellow et al chapter 6.
<ol type="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>
</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>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="material-for-the-active-learning-sessions-on-tuesday-and-wednesday">Material for the active learning sessions on Tuesday and Wednesday </h2>
doc/pub/week41/html/week41.html
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@@ -375,22 +375,29 @@ MathJax.Hub.Config({
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="material-for-the-lecture-on-monday-october-7-2024">Material for the lecture on Monday October 7, 2024 </h2>
<ul>
<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
<!-- * <a href="https://youtu.be/5-RRTO9uDvI" target="_blank">Video of lecture notes</a> -->
<!-- * <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf" target="_blank">Whiteboard notes</a> --></li>
<li> Readings and Videos:</li>
<ul>
<li> These lecture notes</li>
<li> For neural networks we recommend Goodfellow et al chapter 6.</li>
<li> <a href="https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs" target="_blank">Neural Networks demystified</a></li>
<li> <a href="https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex" target="_blank">Building Neural Networks from scratch</a></li>
<li> <a href="https://www.youtube.com/watch?v=CqOfi41LfDw" target="_blank">Video on Neural Networks</a></li>
<li> <a href="https://www.youtube.com/watch?v=Ilg3gGewQ5U" target="_blank">Video on the back propagation algorithm</a></li>
</ul>
</ul>
<p>I 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>
</ol>
<div class="alert alert-block alert-block alert-text-normal">
<b>Readings and Videos:</b>
<p>
<ol>
<li> These lecture notes</li>
<li> Rashcka et al chapter 11</li>
<li> For neural networks we recommend Goodfellow et al chapter 6.
<ol type="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>
</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>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="material-for-the-active-learning-sessions-on-tuesday-and-wednesday">Material for the active learning sessions on Tuesday and Wednesday </h2>
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doc/src/week41/week41.do.txt
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@@ -8,19 +8,20 @@ DATE: Week 41
!split
===== Material for the lecture on Monday October 7, 2024 =====
* Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.
* Building our own Feed-forward Neural Network
o Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model.
o Building our own Feed-forward Neural Network
# * "Video of lecture notes":"https://youtu.be/5-RRTO9uDvI"
# * "Whiteboard notes":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesOctober7.pdf"
* Readings and Videos:
* These lecture notes
* For neural networks we recommend Goodfellow et al chapter 6.
* "Neural Networks demystified":"https://www.youtube.com/watch?v=bxe2T-V8XRs&list=PLiaHhY2iBX9hdHaRr6b7XevZtgZRa1PoU&ab_channel=WelchLabs"
* "Building Neural Networks from scratch":"https://www.youtube.com/watch?v=Wo5dMEP_BbI&list=PLQVvvaa0QuDcjD5BAw2DxE6OF2tius3V3&ab_channel=sentdex"
* "Video on Neural Networks":"https://www.youtube.com/watch?v=CqOfi41LfDw"
* "Video on the back propagation algorithm":"https://www.youtube.com/watch?v=Ilg3gGewQ5U"
I also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at URL:"http://neuralnetworksanddeeplearning.com/chap4.html".
!bblock Readings and Videos:
o These lecture notes
o Rashcka et al chapter 11
o For neural networks we recommend Goodfellow et al chapter 6.
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 Video on Neural Networks at URL:"https://www.youtube.com/watch?v=CqOfi41LfDw"
o Video on the back propagation algorithm at URL:"https://www.youtube.com/watch?v=Ilg3gGewQ5U"
We also recommend Michael Nielsen's intuitive approach to the neural networks and the universal approximation theorem, see the slides at URL:"http://neuralnetworksanddeeplearning.com/chap4.html".
!eblock
!split