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update week 42

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mhjensen
2020-10-16 07:15:44 +02:00
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commit d78f893193
32 changed files with 343 additions and 1529 deletions
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doc/pub/week42/html/._week42-bs000.html
+10 -22
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<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders</a>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</a>
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@@ -162,12 +154,8 @@ MathJax.Hub.Config({
<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
</ul>
</li>
@@ -186,7 +174,7 @@ MathJax.Hub.Config({
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<center><h1>Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders</h1></center> <!-- document title -->
<center><h1>Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</h1></center> <!-- document title -->
<p>
<!-- author(s): Morten Hjorth-Jensen -->
@@ -202,7 +190,7 @@ 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>Oct 15, 2020</h4></center> <!-- date -->
<center><h4>Oct 16, 2020</h4></center> <!-- date -->
<br>
<p>
@@ -226,7 +214,7 @@ MathJax.Hub.Config({
<li><a href="._week42-bs008.html">9</a></li>
<li><a href="._week42-bs009.html">10</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs001.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/week42/html/._week42-bs001.html
+9 -21
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<meta name="generator" content="DocOnce: https://github.com/hplgit/doconce/" />
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<!-- Bootstrap style: bootstrap -->
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@@ -127,7 +119,7 @@ MathJax.Hub.Config({
<span class="icon-bar"></span>
<span class="icon-bar"></span>
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<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders</a>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</a>
</div>
<div class="navbar-collapse collapse navbar-responsive-collapse">
@@ -162,12 +154,8 @@ MathJax.Hub.Config({
<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
</ul>
</li>
@@ -187,7 +175,7 @@ MathJax.Hub.Config({
<ul>
<li> Thursday: Convolutional Neural Networks and examples. <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober15.mp4?vrtx=view-as-webpage" target="_self">Video of Lecture</a></li>
<li> Friday: Recurrent Neural Networks and Autoencoders</li>
<li> Friday: Recurrent Neural Networks. <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober16.mp4?vrtx=view-as-webpage" target="_self">Video of Lecture</a></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 chapters 13 and 14</a>. Autoencoders are discussed in chapter 15 of Geron's text.
@@ -222,7 +210,7 @@ Reading suggestions for both days: <a href="https://github.com/CompPhysics/Machi
<li><a href="._week42-bs009.html">10</a></li>
<li><a href="._week42-bs010.html">11</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs002.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/week42/html/._week42-bs002.html
+8 -20
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<title>Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</title>
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<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders</a>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</a>
</div>
<div class="navbar-collapse collapse navbar-responsive-collapse">
@@ -162,12 +154,8 @@ MathJax.Hub.Config({
<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
</ul>
</li>
@@ -237,7 +225,7 @@ Another good read is the article here <a href="https://arxiv.org/pdf/1603.07285.
<li><a href="._week42-bs010.html">11</a></li>
<li><a href="._week42-bs011.html">12</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs003.html">&raquo;</a></li>
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<!-- ------------------- end of main content --------------- -->
doc/pub/week42/html/._week42-bs003.html
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<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders</a>
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@@ -162,12 +154,8 @@ MathJax.Hub.Config({
<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
</ul>
</li>
@@ -213,7 +201,7 @@ before the transformation.
<li><a href="._week42-bs011.html">12</a></li>
<li><a href="._week42-bs012.html">13</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs004.html">&raquo;</a></li>
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<!-- ------------------- end of main content --------------- -->
doc/pub/week42/html/._week42-bs004.html
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<title>Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</title>
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<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</a>
</div>
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@@ -162,12 +154,8 @@ MathJax.Hub.Config({
<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
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@@ -228,7 +216,7 @@ in the input).
<li><a href="._week42-bs012.html">13</a></li>
<li><a href="._week42-bs013.html">14</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs005.html">&raquo;</a></li>
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<!-- ------------------- end of main content --------------- -->
doc/pub/week42/html/._week42-bs005.html
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<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</a>
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<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
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@@ -229,7 +217,7 @@ would quickly lead to possible overfitting.
<li><a href="._week42-bs013.html">14</a></li>
<li><a href="._week42-bs014.html">15</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs006.html">&raquo;</a></li>
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<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
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@@ -242,7 +230,7 @@ dimension.
<li><a href="._week42-bs014.html">15</a></li>
<li><a href="._week42-bs015.html">16</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
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<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
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<li><a href="._week42-bs015.html">16</a></li>
<li><a href="._week42-bs016.html">17</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs008.html">&raquo;</a></li>
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<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
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<li><a href="._week42-bs016.html">17</a></li>
<li><a href="._week42-bs017.html">18</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs009.html">&raquo;</a></li>
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<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
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<li><a href="._week42-bs017.html">18</a></li>
<li><a href="._week42-bs018.html">19</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs010.html">&raquo;</a></li>
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<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
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<li><a href="._week42-bs018.html">19</a></li>
<li><a href="._week42-bs019.html">20</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
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<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders</a>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</a>
</div>
<div class="navbar-collapse collapse navbar-responsive-collapse">
@@ -162,12 +154,8 @@ MathJax.Hub.Config({
<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
</ul>
</li>
@@ -218,7 +206,7 @@ $$
<li><a href="._week42-bs019.html">20</a></li>
<li><a href="._week42-bs020.html">21</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs012.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/week42/html/._week42-bs012.html
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@@ -127,7 +119,7 @@ MathJax.Hub.Config({
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders</a>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</a>
</div>
<div class="navbar-collapse collapse navbar-responsive-collapse">
@@ -162,12 +154,8 @@ MathJax.Hub.Config({
<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
</ul>
</li>
@@ -224,7 +212,7 @@ single neuron in the first hidden layer.
<li><a href="._week42-bs020.html">21</a></li>
<li><a href="._week42-bs021.html">22</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs013.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/week42/html/._week42-bs013.html
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@@ -127,7 +119,7 @@ MathJax.Hub.Config({
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders</a>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</a>
</div>
<div class="navbar-collapse collapse navbar-responsive-collapse">
@@ -162,12 +154,8 @@ MathJax.Hub.Config({
<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
</ul>
</li>
@@ -224,7 +212,7 @@ fixed, and known as a <a href="https://en.wikipedia.org/wiki/Receptive_field" ta
<li><a href="._week42-bs021.html">22</a></li>
<li><a href="._week42-bs022.html">23</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs014.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/week42/html/._week42-bs014.html
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<title>Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</title>
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@@ -127,7 +119,7 @@ MathJax.Hub.Config({
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders</a>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</a>
</div>
<div class="navbar-collapse collapse navbar-responsive-collapse">
@@ -162,12 +154,8 @@ MathJax.Hub.Config({
<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
</ul>
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@@ -228,7 +216,7 @@ layer.
<li><a href="._week42-bs022.html">23</a></li>
<li><a href="._week42-bs023.html">24</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs015.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/week42/html/._week42-bs015.html
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@@ -127,7 +119,7 @@ MathJax.Hub.Config({
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders</a>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</a>
</div>
<div class="navbar-collapse collapse navbar-responsive-collapse">
@@ -162,12 +154,8 @@ MathJax.Hub.Config({
<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
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@@ -221,7 +209,7 @@ classification.
<li><a href="._week42-bs023.html">24</a></li>
<li><a href="._week42-bs024.html">25</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs016.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/week42/html/._week42-bs016.html
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<span class="icon-bar"></span>
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</button>
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<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</a>
</div>
<div class="navbar-collapse collapse navbar-responsive-collapse">
@@ -162,12 +154,8 @@ MathJax.Hub.Config({
<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
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@@ -254,7 +242,7 @@ plt<span style="color: #666666">.</span>show()
<li><a href="._week42-bs024.html">25</a></li>
<li><a href="._week42-bs025.html">26</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs017.html">&raquo;</a></li>
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<!-- ------------------- end of main content --------------- -->
doc/pub/week42/html/._week42-bs017.html
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('Set up of an RNN', 2, None, '___sec27'),
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@@ -127,7 +119,7 @@ MathJax.Hub.Config({
<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders</a>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</a>
</div>
<div class="navbar-collapse collapse navbar-responsive-collapse">
@@ -162,12 +154,8 @@ MathJax.Hub.Config({
<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
</ul>
</li>
@@ -236,7 +224,7 @@ X_train, X_test, Y_train, Y_test <span style="color: #666666">=</span> train_tes
<li><a href="._week42-bs025.html">26</a></li>
<li><a href="._week42-bs026.html">27</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs018.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/week42/html/._week42-bs018.html
+8 -20
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<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders</a>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</a>
</div>
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@@ -162,12 +154,8 @@ MathJax.Hub.Config({
<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
</ul>
</li>
@@ -241,7 +229,7 @@ lmbd_vals <span style="color: #666666">=</span> np<span style="color: #666666">.
<li><a href="._week42-bs026.html">27</a></li>
<li><a href="._week42-bs027.html">28</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs019.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/week42/html/._week42-bs019.html
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<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders</a>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</a>
</div>
<div class="navbar-collapse collapse navbar-responsive-collapse">
@@ -162,12 +154,8 @@ MathJax.Hub.Config({
<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
</ul>
</li>
@@ -231,7 +219,7 @@ MathJax.Hub.Config({
<li><a href="._week42-bs027.html">28</a></li>
<li><a href="._week42-bs028.html">29</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs020.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/week42/html/._week42-bs020.html
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<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders</a>
<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</a>
</div>
<div class="navbar-collapse collapse navbar-responsive-collapse">
@@ -162,12 +154,8 @@ MathJax.Hub.Config({
<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
</ul>
</li>
@@ -244,8 +232,6 @@ plt<span style="color: #666666">.</span>show()
<li><a href="._week42-bs027.html">28</a></li>
<li><a href="._week42-bs028.html">29</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs021.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/week42/html/._week42-bs021.html
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<span class="icon-bar"></span>
<span class="icon-bar"></span>
</button>
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<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</a>
</div>
<div class="navbar-collapse collapse navbar-responsive-collapse">
@@ -162,12 +154,8 @@ MathJax.Hub.Config({
<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
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@@ -229,9 +217,6 @@ train_images, test_images <span style="color: #666666">=</span> train_images <sp
<li><a href="._week42-bs027.html">28</a></li>
<li><a href="._week42-bs028.html">29</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs030.html">31</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs022.html">&raquo;</a></li>
</ul>
<!-- ------------------- end of main content --------------- -->
doc/pub/week42/html/._week42-bs022.html
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<a class="navbar-brand" href="week42-bs.html">Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</a>
</div>
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@@ -162,12 +154,8 @@ MathJax.Hub.Config({
<!-- navigation toc: --> <li><a href="._week42-bs025.html#___sec24" style="font-size: 80%;">Compile and train the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs031.html#___sec30" style="font-size: 80%;">Long-Short Time Memory</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs033.html#___sec32" style="font-size: 80%;">Simple examples of Autoencoders</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">A simple example</a></li>
</ul>
</li>
@@ -229,10 +217,6 @@ plt<span style="color: #666666">.</span>show()
<li><a href="._week42-bs027.html">28</a></li>
<li><a href="._week42-bs028.html">29</a></li>
<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs030.html">31</a></li>
<li><a href="._week42-bs031.html">32</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs023.html">&raquo;</a></li>
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<!-- ------------------- end of main content --------------- -->
doc/pub/week42/html/._week42-bs023.html
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<!-- navigation toc: --> <li><a href="._week42-bs026.html#___sec25" style="font-size: 80%;">Finally, evaluate the model</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
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<!-- navigation toc: --> <li><a href="._week42-bs032.html#___sec31" style="font-size: 80%;">Autoencoders: Overarching view</a></li>
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<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">Set up of an RNN</a></li>
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@@ -230,11 +218,6 @@ You can see that the output of every Conv2D and MaxPooling2D layer is a 3D tenso
<li><a href="._week42-bs027.html">28</a></li>
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<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs030.html">31</a></li>
<li><a href="._week42-bs031.html">32</a></li>
<li><a href="._week42-bs032.html">33</a></li>
<li><a href="">...</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs024.html">&raquo;</a></li>
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doc/pub/week42/html/._week42-bs024.html
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<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
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<li><a href="._week42-bs032.html">33</a></li>
<li><a href="._week42-bs033.html">34</a></li>
<li><a href="._week42-bs025.html">&raquo;</a></li>
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<!-- navigation toc: --> <li><a href="._week42-bs027.html#___sec26" style="font-size: 80%;">Recurrent neural networks: Overarching view</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs028.html#___sec27" style="font-size: 80%;">A simple example</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs029.html#___sec28" style="font-size: 80%;">Set up of an RNN</a></li>
<!-- navigation toc: --> <li><a href="._week42-bs030.html#___sec29" style="font-size: 80%;">Solving differential equations and eigenvalue problems with RNNs</a></li>
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<p>
<!-- author(s): Morten Hjorth-Jensen -->
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<li><a href="._week42-bs029.html">30</a></li>
<li><a href="._week42-bs001.html">&raquo;</a></li>
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doc/pub/week42/html/week42-reveal.html
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<p>
<!-- author(s): Morten Hjorth-Jensen -->
@@ -148,7 +148,7 @@ MathJax.Hub.Config({
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<br>
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<center><h4>Oct 16, 2020</h4></center> <!-- date -->
<br>
<p>
@@ -163,7 +163,7 @@ MathJax.Hub.Config({
<ul>
<p><li> Thursday: Convolutional Neural Networks and examples. <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober15.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a></li>
<p><li> Friday: Recurrent Neural Networks and Autoencoders</li>
<p><li> Friday: Recurrent Neural Networks. <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober16.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a></li>
</ul>
<p>
@@ -851,7 +851,18 @@ systems such as automatic translation and speech-to-text.
<section>
<h2 id="___sec27">A simple example </h2>
<h2 id="___sec27">Set up of an RNN </h2>
<p>
See the <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesOctober16.pdf" target="_blank">handwritten notes</a> and the <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober16.mp4?vrtx=view-as-webpage" target="_blank">video from the lecture of October 16</a>.
<p>
More text will be added later.
</section>
<section>
<h2 id="___sec28">A simple example </h2>
<p>
@@ -928,88 +939,6 @@ plt.show()
</section>
<section>
<h2 id="___sec28">Set up of an RNN </h2>
<p>
The figure here displays a simple example of an RNN, with inputs \( x_t \)
at a given time \( t \) and outputs \( y_t \). Introducing time as a variable
offers an intutitive way of understanding these networks. In addition
to the inputs \( x_t \), the layer at a time \( t \) receives also as input
the output from the previous layer \( t-1 \), that is \( y_{t1} \).
<p>
This means also that we need to have weights that link both the inputs
\( x_t \) to the outputs \( y_t \) as well as weights that link the output
from the previous time \( y_{t-1} \) and \( y_t \). The figure here shows an
example of a simple RNN.
<p>
More material will be added here.
</section>
<section>
<h2 id="___sec29">Solving differential equations and eigenvalue problems with RNNs </h2>
<p>
In our discussions of ordinary differential equations and partial
differential equations using neural networks. Here we will discuss how
we can solve say ordinary differential equations and eigenvalue
problems using RNNs. Eigenvalue problems can be solved using RNNs by
rewriting such a problems as a non-linear differential equation.
<p>
Instead of starting with a well-known ordinary differential equation,
we start directly with an eigenvaule problem.
</section>
<section>
<h2 id="___sec30">Long-Short Time Memory </h2>
<p>
Discussions about dynamic unrolling through time. discuss memory cells, input and output
</section>
<section>
<h2 id="___sec31">Autoencoders: Overarching view </h2>
<p>
Autoencoders are artificial neural networks capable of learning
efficient representations of the input data (these representations are called codings) without
any supervision (i.e., the training set is unlabeled). These codings
typically have a much lower dimensionality than the input data, making
autoencoders useful for dimensionality reduction.
<p>
More importantly, autoencoders act as powerful feature detectors, and
they can be used for unsupervised pretraining of deep neural networks.
<p>
Lastly, they are capable of randomly generating new data that looks
very similar to the training data; this is called a generative
model. For example, you could train an autoencoder on pictures of
faces, and it would then be able to generate new faces. Surprisingly,
autoencoders work by simply learning to copy their inputs to their
outputs. This may sound like a trivial task, but we will see that
constraining the network in various ways can make it rather
difficult. For example, you can limit the size of the internal
representation, or you can add noise to the inputs and train the
network to recover the original inputs. These constraints prevent the
autoencoder from trivially copying the inputs directly to the outputs,
which forces it to learn efficient ways of representing the data. In
short, the codings are byproducts of the autoencoder&#8217;s attempt to
learn the identity function under some constraints.
</section>
<section>
<h2 id="___sec32">Simple examples of Autoencoders </h2>
</section>
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@@ -143,7 +135,7 @@ MathJax.Hub.Config({
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<center><h1>Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</h1></center> <!-- document title -->
<p>
<!-- author(s): Morten Hjorth-Jensen -->
@@ -159,7 +151,7 @@ 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>Oct 15, 2020</h4></center> <!-- date -->
<center><h4>Oct 16, 2020</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -168,7 +160,7 @@ MathJax.Hub.Config({
<ul>
<li> Thursday: Convolutional Neural Networks and examples. <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober15.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a></li>
<li> Friday: Recurrent Neural Networks and Autoencoders</li>
<li> Friday: Recurrent Neural Networks. <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober16.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a></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 chapters 13 and 14</a>. Autoencoders are discussed in chapter 15 of Geron's text.
@@ -843,7 +835,18 @@ systems such as automatic translation and speech-to-text.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec27">A simple example </h2>
<h2 id="___sec27">Set up of an RNN </h2>
<p>
See the <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesOctober16.pdf" target="_blank">handwritten notes</a> and the <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober16.mp4?vrtx=view-as-webpage" target="_blank">video from the lecture of October 16</a>.
<p>
More text will be added later.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec28">A simple example </h2>
<p>
@@ -917,88 +920,6 @@ plt.plot(index,predicted)
plt.axvline(df.index[Tp], c=<span style="color: #CD5555">&quot;r&quot;</span>)
plt.show()
</pre></div>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec28">Set up of an RNN </h2>
<p>
The figure here displays a simple example of an RNN, with inputs \( x_t \)
at a given time \( t \) and outputs \( y_t \). Introducing time as a variable
offers an intutitive way of understanding these networks. In addition
to the inputs \( x_t \), the layer at a time \( t \) receives also as input
the output from the previous layer \( t-1 \), that is \( y_{t1} \).
<p>
This means also that we need to have weights that link both the inputs
\( x_t \) to the outputs \( y_t \) as well as weights that link the output
from the previous time \( y_{t-1} \) and \( y_t \). The figure here shows an
example of a simple RNN.
<p>
More material will be added here.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec29">Solving differential equations and eigenvalue problems with RNNs </h2>
<p>
In our discussions of ordinary differential equations and partial
differential equations using neural networks. Here we will discuss how
we can solve say ordinary differential equations and eigenvalue
problems using RNNs. Eigenvalue problems can be solved using RNNs by
rewriting such a problems as a non-linear differential equation.
<p>
Instead of starting with a well-known ordinary differential equation,
we start directly with an eigenvaule problem.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec30">Long-Short Time Memory </h2>
<p>
Discussions about dynamic unrolling through time. discuss memory cells, input and output
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec31">Autoencoders: Overarching view </h2>
<p>
Autoencoders are artificial neural networks capable of learning
efficient representations of the input data (these representations are called codings) without
any supervision (i.e., the training set is unlabeled). These codings
typically have a much lower dimensionality than the input data, making
autoencoders useful for dimensionality reduction.
<p>
More importantly, autoencoders act as powerful feature detectors, and
they can be used for unsupervised pretraining of deep neural networks.
<p>
Lastly, they are capable of randomly generating new data that looks
very similar to the training data; this is called a generative
model. For example, you could train an autoencoder on pictures of
faces, and it would then be able to generate new faces. Surprisingly,
autoencoders work by simply learning to copy their inputs to their
outputs. This may sound like a trivial task, but we will see that
constraining the network in various ways can make it rather
difficult. For example, you can limit the size of the internal
representation, or you can add noise to the inputs and train the
network to recover the original inputs. These constraints prevent the
autoencoder from trivially copying the inputs directly to the outputs,
which forces it to learn efficient ways of representing the data. In
short, the codings are byproducts of the autoencoder&#8217;s attempt to
learn the identity function under some constraints.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec32">Simple examples of Autoencoders </h2>
<p>
<!-- ------------------- end of main content --------------- -->
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<title>Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</title>
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@@ -148,7 +140,7 @@ MathJax.Hub.Config({
<center><h1>Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders</h1></center> <!-- document title -->
<center><h1>Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks</h1></center> <!-- document title -->
<p>
<!-- author(s): Morten Hjorth-Jensen -->
@@ -164,7 +156,7 @@ 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>Oct 15, 2020</h4></center> <!-- date -->
<center><h4>Oct 16, 2020</h4></center> <!-- date -->
<br>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
@@ -173,7 +165,7 @@ MathJax.Hub.Config({
<ul>
<li> Thursday: Convolutional Neural Networks and examples. <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober15.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a></li>
<li> Friday: Recurrent Neural Networks and Autoencoders</li>
<li> Friday: Recurrent Neural Networks. <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober16.mp4?vrtx=view-as-webpage" target="_blank">Video of Lecture</a></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 chapters 13 and 14</a>. Autoencoders are discussed in chapter 15 of Geron's text.
@@ -848,7 +840,18 @@ systems such as automatic translation and speech-to-text.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec27">A simple example </h2>
<h2 id="___sec27">Set up of an RNN </h2>
<p>
See the <a href="https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesOctober16.pdf" target="_blank">handwritten notes</a> and the <a href="https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober16.mp4?vrtx=view-as-webpage" target="_blank">video from the lecture of October 16</a>.
<p>
More text will be added later.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec28">A simple example </h2>
<p>
@@ -922,88 +925,6 @@ plt<span style="color: #666666">.</span>plot(index,predicted)
plt<span style="color: #666666">.</span>axvline(df<span style="color: #666666">.</span>index[Tp], c<span style="color: #666666">=</span><span style="color: #BA2121">&quot;r&quot;</span>)
plt<span style="color: #666666">.</span>show()
</pre></div>
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec28">Set up of an RNN </h2>
<p>
The figure here displays a simple example of an RNN, with inputs \( x_t \)
at a given time \( t \) and outputs \( y_t \). Introducing time as a variable
offers an intutitive way of understanding these networks. In addition
to the inputs \( x_t \), the layer at a time \( t \) receives also as input
the output from the previous layer \( t-1 \), that is \( y_{t1} \).
<p>
This means also that we need to have weights that link both the inputs
\( x_t \) to the outputs \( y_t \) as well as weights that link the output
from the previous time \( y_{t-1} \) and \( y_t \). The figure here shows an
example of a simple RNN.
<p>
More material will be added here.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec29">Solving differential equations and eigenvalue problems with RNNs </h2>
<p>
In our discussions of ordinary differential equations and partial
differential equations using neural networks. Here we will discuss how
we can solve say ordinary differential equations and eigenvalue
problems using RNNs. Eigenvalue problems can be solved using RNNs by
rewriting such a problems as a non-linear differential equation.
<p>
Instead of starting with a well-known ordinary differential equation,
we start directly with an eigenvaule problem.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec30">Long-Short Time Memory </h2>
<p>
Discussions about dynamic unrolling through time. discuss memory cells, input and output
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec31">Autoencoders: Overarching view </h2>
<p>
Autoencoders are artificial neural networks capable of learning
efficient representations of the input data (these representations are called codings) without
any supervision (i.e., the training set is unlabeled). These codings
typically have a much lower dimensionality than the input data, making
autoencoders useful for dimensionality reduction.
<p>
More importantly, autoencoders act as powerful feature detectors, and
they can be used for unsupervised pretraining of deep neural networks.
<p>
Lastly, they are capable of randomly generating new data that looks
very similar to the training data; this is called a generative
model. For example, you could train an autoencoder on pictures of
faces, and it would then be able to generate new faces. Surprisingly,
autoencoders work by simply learning to copy their inputs to their
outputs. This may sound like a trivial task, but we will see that
constraining the network in various ways can make it rather
difficult. For example, you can limit the size of the internal
representation, or you can add noise to the inputs and train the
network to recover the original inputs. These constraints prevent the
autoencoder from trivially copying the inputs directly to the outputs,
which forces it to learn efficient ways of representing the data. In
short, the codings are byproducts of the autoencoder&#8217;s attempt to
learn the identity function under some constraints.
<p>
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
<h2 id="___sec32">Simple examples of Autoencoders </h2>
<p>
<!-- ------------------- end of main content --------------- -->
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@@ -1,4 +1,4 @@
TITLE: Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders
TITLE: Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks
AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
DATE: today
@@ -7,7 +7,7 @@ DATE: today
===== Plan for week 42 =====
* Thursday: Convolutional Neural Networks and examples. "Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober15.mp4?vrtx=view-as-webpage"
* Friday: Recurrent Neural Networks and Autoencoders
* Friday: Recurrent Neural Networks. "Video of Lecture":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober16.mp4?vrtx=view-as-webpage"
Reading suggestions for both days: "Aurelien Geron's chapters 13 and 14":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/Textbooks/TensorflowML.pdf". Autoencoders are discussed in chapter 15 of Geron's text.
@@ -594,6 +594,16 @@ input, making them extremely useful for natural language processing
systems such as automatic translation and speech-to-text.
!split
===== Set up of an RNN =====
See the "handwritten notes":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/NotesOctober16.pdf" and the "video from the lecture of October 16":"https://www.uio.no/studier/emner/matnat/fys/FYS-STK4155/h20/forelesningsvideoer/LectureOctober16.mp4?vrtx=view-as-webpage".
More text will be added later.
!split
===== A simple example =====
@@ -669,77 +679,3 @@ plt.show()
!ec
!split
===== Set up of an RNN =====
The figure here displays a simple example of an RNN, with inputs $x_t$
at a given time $t$ and outputs $y_t$. Introducing time as a variable
offers an intutitive way of understanding these networks. In addition
to the inputs $x_t$, the layer at a time $t$ receives also as input
the output from the previous layer $t-1$, that is $y_{t1}$.
This means also that we need to have weights that link both the inputs
$x_t$ to the outputs $y_t$ as well as weights that link the output
from the previous time $y_{t-1}$ and $y_t$. The figure here shows an
example of a simple RNN.
More material will be added here.
!split
===== Solving differential equations and eigenvalue problems with RNNs =====
In our discussions of ordinary differential equations and partial
differential equations using neural networks. Here we will discuss how
we can solve say ordinary differential equations and eigenvalue
problems using RNNs. Eigenvalue problems can be solved using RNNs by
rewriting such a problems as a non-linear differential equation.
Instead of starting with a well-known ordinary differential equation,
we start directly with an eigenvaule problem.
!split
===== Long-Short Time Memory =====
Discussions about dynamic unrolling through time. discuss memory cells, input and output
!split
===== Autoencoders: Overarching view =====
Autoencoders are artificial neural networks capable of learning
efficient representations of the input data (these representations are called codings) without
any supervision (i.e., the training set is unlabeled). These codings
typically have a much lower dimensionality than the input data, making
autoencoders useful for dimensionality reduction.
More importantly, autoencoders act as powerful feature detectors, and
they can be used for unsupervised pretraining of deep neural networks.
Lastly, they are capable of randomly generating new data that looks
very similar to the training data; this is called a generative
model. For example, you could train an autoencoder on pictures of
faces, and it would then be able to generate new faces. Surprisingly,
autoencoders work by simply learning to copy their inputs to their
outputs. This may sound like a trivial task, but we will see that
constraining the network in various ways can make it rather
difficult. For example, you can limit the size of the internal
representation, or you can add noise to the inputs and train the
network to recover the original inputs. These constraints prevent the
autoencoder from trivially copying the inputs directly to the outputs,
which forces it to learn efficient ways of representing the data. In
short, the codings are byproducts of the autoencoder’s attempt to
learn the identity function under some constraints.
!split
===== Simple examples of Autoencoders =====