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update of week 40

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mhjensen
2020-09-30 21:56:51 +02:00
parent 4dd84d8714
commit 8ae6436fcd
9 changed files with 18 additions and 18 deletions
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doc/pub/week40/html/._week40-bs000.html
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@@ -254,7 +254,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Sep 29, 2020</h4></center> <!-- date -->
<center><h4>Sep 30, 2020</h4></center> <!-- date -->
<br>
<p>
doc/pub/week40/html/._week40-bs001.html
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@@ -238,8 +238,8 @@ MathJax.Hub.Config({
<h2 id="___sec0" class="anchor">Plan for week 40 </h2>
<ul>
<li> Thursday: Stochastic Gradient descent with examples and automatic differeantion and begin Neural Networks</li>
<li> Friday: Neural Networks, setting up the basic steps</li>
<li> Thursday: Stochastic Gradient descent with examples and automatic differentiation and begin Neural Networks</li>
<li> Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model</li>
</ul>
Reading suggestions for both days: <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" target="_self">Aurelien Geron's chapter 10</a> and Hastie et al chapter 11.
doc/pub/week40/html/week40-bs.html
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@@ -254,7 +254,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Sep 29, 2020</h4></center> <!-- date -->
<center><h4>Sep 30, 2020</h4></center> <!-- date -->
<br>
<p>
doc/pub/week40/html/week40-reveal.html
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@@ -148,7 +148,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>&nbsp;<br>
<center><h4>Sep 29, 2020</h4></center> <!-- date -->
<center><h4>Sep 30, 2020</h4></center> <!-- date -->
<br>
<p>
@@ -162,8 +162,8 @@ MathJax.Hub.Config({
<h2 id="___sec0">Plan for week 40 </h2>
<ul>
<p><li> Thursday: Stochastic Gradient descent with examples and automatic differeantion and begin Neural Networks</li>
<p><li> Friday: Neural Networks, setting up the basic steps</li>
<p><li> Thursday: Stochastic Gradient descent with examples and automatic differentiation and begin Neural Networks</li>
<p><li> Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model</li>
</ul>
<p>
doc/pub/week40/html/week40-solarized.html
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@@ -186,7 +186,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Sep 29, 2020</h4></center> <!-- date -->
<center><h4>Sep 30, 2020</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -194,8 +194,8 @@ MathJax.Hub.Config({
<h2 id="___sec0">Plan for week 40 </h2>
<ul>
<li> Thursday: Stochastic Gradient descent with examples and automatic differeantion and begin Neural Networks</li>
<li> Friday: Neural Networks, setting up the basic steps</li>
<li> Thursday: Stochastic Gradient descent with examples and automatic differentiation and begin Neural Networks</li>
<li> Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model</li>
</ul>
Reading suggestions for both days: <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" target="_blank">Aurelien Geron's chapter 10</a> and Hastie et al chapter 11.
doc/pub/week40/html/week40.html
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@@ -191,7 +191,7 @@ MathJax.Hub.Config({
<center>[2] <b>Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University</b></center>
<br>
<p>
<center><h4>Sep 29, 2020</h4></center> <!-- date -->
<center><h4>Sep 30, 2020</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -199,8 +199,8 @@ MathJax.Hub.Config({
<h2 id="___sec0">Plan for week 40 </h2>
<ul>
<li> Thursday: Stochastic Gradient descent with examples and automatic differeantion and begin Neural Networks</li>
<li> Friday: Neural Networks, setting up the basic steps</li>
<li> Thursday: Stochastic Gradient descent with examples and automatic differentiation and begin Neural Networks</li>
<li> Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model</li>
</ul>
Reading suggestions for both days: <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" target="_blank">Aurelien Geron's chapter 10</a> and Hastie et al chapter 11.
doc/pub/week40/ipynb/ipynb-week40-src.tar.gz
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doc/pub/week40/ipynb/week40.ipynb
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@@ -10,7 +10,7 @@
"<!-- Author: --> \n",
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
"\n",
"Date: **Sep 29, 2020**\n",
"Date: **Sep 30, 2020**\n",
"\n",
"Copyright 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
@@ -19,9 +19,9 @@
"\n",
"## Plan for week 40\n",
"\n",
"* Thursday: Stochastic Gradient descent with examples and automatic differeantion and begin Neural Networks \n",
"* Thursday: Stochastic Gradient descent with examples and automatic differentiation and begin Neural Networks \n",
"\n",
"* Friday: Neural Networks, setting up the basic steps\n",
"* Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model\n",
"\n",
"Reading suggestions for both days: [Aurelien Geron's chapter 10](https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf) and Hastie et al chapter 11.\n",
"For Stochastic Gradient Descent, we recommend chapter 4 of Geron's text. \n",
doc/src/week40/week40.do.txt
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@@ -6,8 +6,8 @@ DATE: today
!split
===== Plan for week 40 =====
* Thursday: Stochastic Gradient descent with examples and automatic differeantion and begin Neural Networks
* Friday: Neural Networks, setting up the basic steps
* Thursday: Stochastic Gradient descent with examples and automatic differentiation and begin Neural Networks
* Friday: Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model
Reading suggestions for both days: "Aurelien Geron's chapter 10":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf" and Hastie et al chapter 11.
For Stochastic Gradient Descent, we recommend chapter 4 of Geron's text.