diff --git a/doc/pub/week42/html/._week42-bs000.html b/doc/pub/week42/html/._week42-bs000.html index 8db429738..80d4c12ae 100644 --- a/doc/pub/week42/html/._week42-bs000.html +++ b/doc/pub/week42/html/._week42-bs000.html @@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source - + -Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders +Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks @@ -88,16 +88,8 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec26'), - ('A simple example', 2, None, '___sec27'), - ('Set up of an RNN', 2, None, '___sec28'), - ('Solving differential equations and eigenvalue problems with ' - 'RNNs', - 2, - None, - '___sec29'), - ('Long-Short Time Memory', 2, None, '___sec30'), - ('Autoencoders: Overarching view', 2, None, '___sec31'), - ('Simple examples of Autoencoders', 2, None, '___sec32')]} + ('Set up of an RNN', 2, None, '___sec27'), + ('A simple example', 2, None, '___sec28')]} end of tocinfo --> @@ -127,7 +119,7 @@ MathJax.Hub.Config({ - Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders + Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks
@@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -186,7 +174,7 @@ MathJax.Hub.Config({
    -

    Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders

    +

    Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks

    @@ -202,7 +190,7 @@ MathJax.Hub.Config({

    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

    -

    Oct 15, 2020

    +

    Oct 16, 2020


    @@ -226,7 +214,7 @@ MathJax.Hub.Config({

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    • diff --git a/doc/pub/week42/html/._week42-bs001.html b/doc/pub/week42/html/._week42-bs001.html index 6f3bd96a1..4925a04a6 100644 --- a/doc/pub/week42/html/._week42-bs001.html +++ b/doc/pub/week42/html/._week42-bs001.html @@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source - + -Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders +Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks @@ -88,16 +88,8 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec26'), - ('A simple example', 2, None, '___sec27'), - ('Set up of an RNN', 2, None, '___sec28'), - ('Solving differential equations and eigenvalue problems with ' - 'RNNs', - 2, - None, - '___sec29'), - ('Long-Short Time Memory', 2, None, '___sec30'), - ('Autoencoders: Overarching view', 2, None, '___sec31'), - ('Simple examples of Autoencoders', 2, None, '___sec32')]} + ('Set up of an RNN', 2, None, '___sec27'), + ('A simple example', 2, None, '___sec28')]} end of tocinfo --> @@ -127,7 +119,7 @@ MathJax.Hub.Config({ - Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders + Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks
    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -187,7 +175,7 @@ MathJax.Hub.Config({ Reading suggestions for both days: Aurelien Geron's chapters 13 and 14. Autoencoders are discussed in chapter 15 of Geron's text. @@ -222,7 +210,7 @@ Reading suggestions for both days: 10
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    • diff --git a/doc/pub/week42/html/._week42-bs002.html b/doc/pub/week42/html/._week42-bs002.html index 0d8278af7..b66be4f85 100644 --- a/doc/pub/week42/html/._week42-bs002.html +++ b/doc/pub/week42/html/._week42-bs002.html @@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source - + -Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders +Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks @@ -88,16 +88,8 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec26'), - ('A simple example', 2, None, '___sec27'), - ('Set up of an RNN', 2, None, '___sec28'), - ('Solving differential equations and eigenvalue problems with ' - 'RNNs', - 2, - None, - '___sec29'), - ('Long-Short Time Memory', 2, None, '___sec30'), - ('Autoencoders: Overarching view', 2, None, '___sec31'), - ('Simple examples of Autoencoders', 2, None, '___sec32')]} + ('Set up of an RNN', 2, None, '___sec27'), + ('A simple example', 2, None, '___sec28')]} end of tocinfo --> @@ -127,7 +119,7 @@ MathJax.Hub.Config({ - Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders + Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks
    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -237,7 +225,7 @@ Another good read is the article here 11
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    • diff --git a/doc/pub/week42/html/._week42-bs003.html b/doc/pub/week42/html/._week42-bs003.html index 3cea4f709..517a0d942 100644 --- a/doc/pub/week42/html/._week42-bs003.html +++ b/doc/pub/week42/html/._week42-bs003.html @@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source - + -Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders +Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks @@ -88,16 +88,8 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec26'), - ('A simple example', 2, None, '___sec27'), - ('Set up of an RNN', 2, None, '___sec28'), - ('Solving differential equations and eigenvalue problems with ' - 'RNNs', - 2, - None, - '___sec29'), - ('Long-Short Time Memory', 2, None, '___sec30'), - ('Autoencoders: Overarching view', 2, None, '___sec31'), - ('Simple examples of Autoencoders', 2, None, '___sec32')]} + ('Set up of an RNN', 2, None, '___sec27'), + ('A simple example', 2, None, '___sec28')]} end of tocinfo --> @@ -127,7 +119,7 @@ MathJax.Hub.Config({ - Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders + Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks
    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -213,7 +201,7 @@ before the transformation.
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    • diff --git a/doc/pub/week42/html/._week42-bs004.html b/doc/pub/week42/html/._week42-bs004.html index 0fec67451..f2286bc98 100644 --- a/doc/pub/week42/html/._week42-bs004.html +++ b/doc/pub/week42/html/._week42-bs004.html @@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source - + -Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders +Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks @@ -88,16 +88,8 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec26'), - ('A simple example', 2, None, '___sec27'), - ('Set up of an RNN', 2, None, '___sec28'), - ('Solving differential equations and eigenvalue problems with ' - 'RNNs', - 2, - None, - '___sec29'), - ('Long-Short Time Memory', 2, None, '___sec30'), - ('Autoencoders: Overarching view', 2, None, '___sec31'), - ('Simple examples of Autoencoders', 2, None, '___sec32')]} + ('Set up of an RNN', 2, None, '___sec27'), + ('A simple example', 2, None, '___sec28')]} end of tocinfo --> @@ -127,7 +119,7 @@ MathJax.Hub.Config({ - Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders + Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks
    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -228,7 +216,7 @@ in the input).
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    • diff --git a/doc/pub/week42/html/._week42-bs005.html b/doc/pub/week42/html/._week42-bs005.html index 0e2c0e6e4..04ddd2f77 100644 --- a/doc/pub/week42/html/._week42-bs005.html +++ b/doc/pub/week42/html/._week42-bs005.html @@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source - + -Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders +Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks @@ -88,16 +88,8 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec26'), - ('A simple example', 2, None, '___sec27'), - ('Set up of an RNN', 2, None, '___sec28'), - ('Solving differential equations and eigenvalue problems with ' - 'RNNs', - 2, - None, - '___sec29'), - ('Long-Short Time Memory', 2, None, '___sec30'), - ('Autoencoders: Overarching view', 2, None, '___sec31'), - ('Simple examples of Autoencoders', 2, None, '___sec32')]} + ('Set up of an RNN', 2, None, '___sec27'), + ('A simple example', 2, None, '___sec28')]} end of tocinfo --> @@ -127,7 +119,7 @@ MathJax.Hub.Config({ - Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders + Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks
    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -229,7 +217,7 @@ would quickly lead to possible overfitting.
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    • diff --git a/doc/pub/week42/html/._week42-bs006.html b/doc/pub/week42/html/._week42-bs006.html index 60887e89b..444489b28 100644 --- a/doc/pub/week42/html/._week42-bs006.html +++ b/doc/pub/week42/html/._week42-bs006.html @@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source - + -Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders +Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks @@ -88,16 +88,8 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec26'), - ('A simple example', 2, None, '___sec27'), - ('Set up of an RNN', 2, None, '___sec28'), - ('Solving differential equations and eigenvalue problems with ' - 'RNNs', - 2, - None, - '___sec29'), - ('Long-Short Time Memory', 2, None, '___sec30'), - ('Autoencoders: Overarching view', 2, None, '___sec31'), - ('Simple examples of Autoencoders', 2, None, '___sec32')]} + ('Set up of an RNN', 2, None, '___sec27'), + ('A simple example', 2, None, '___sec28')]} end of tocinfo --> @@ -127,7 +119,7 @@ MathJax.Hub.Config({ - Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders + Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks
    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -242,7 +230,7 @@ dimension.
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    • diff --git a/doc/pub/week42/html/._week42-bs007.html b/doc/pub/week42/html/._week42-bs007.html index 7dc796b2f..0cedc966b 100644 --- a/doc/pub/week42/html/._week42-bs007.html +++ b/doc/pub/week42/html/._week42-bs007.html @@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source - + -Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders +Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks @@ -88,16 +88,8 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec26'), - ('A simple example', 2, None, '___sec27'), - ('Set up of an RNN', 2, None, '___sec28'), - ('Solving differential equations and eigenvalue problems with ' - 'RNNs', - 2, - None, - '___sec29'), - ('Long-Short Time Memory', 2, None, '___sec30'), - ('Autoencoders: Overarching view', 2, None, '___sec31'), - ('Simple examples of Autoencoders', 2, None, '___sec32')]} + ('Set up of an RNN', 2, None, '___sec27'), + ('A simple example', 2, None, '___sec28')]} end of tocinfo --> @@ -127,7 +119,7 @@ MathJax.Hub.Config({ - Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders + Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks
    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -226,7 +214,7 @@ A simple CNN for image classification could have the architecture:
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    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -223,7 +211,7 @@ are consistent with the labels in the training set for each image.
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    • diff --git a/doc/pub/week42/html/._week42-bs009.html b/doc/pub/week42/html/._week42-bs009.html index 8db715089..120bb635f 100644 --- a/doc/pub/week42/html/._week42-bs009.html +++ b/doc/pub/week42/html/._week42-bs009.html @@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source - + -Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders +Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks @@ -88,16 +88,8 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec26'), - ('A simple example', 2, None, '___sec27'), - ('Set up of an RNN', 2, None, '___sec28'), - ('Solving differential equations and eigenvalue problems with ' - 'RNNs', - 2, - None, - '___sec29'), - ('Long-Short Time Memory', 2, None, '___sec30'), - ('Autoencoders: Overarching view', 2, None, '___sec31'), - ('Simple examples of Autoencoders', 2, None, '___sec32')]} + ('Set up of an RNN', 2, None, '___sec27'), + ('A simple example', 2, None, '___sec28')]} end of tocinfo --> @@ -127,7 +119,7 @@ MathJax.Hub.Config({ - Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders + Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks
    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -226,7 +214,7 @@ and the slides of 18
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    • diff --git a/doc/pub/week42/html/._week42-bs010.html b/doc/pub/week42/html/._week42-bs010.html index b30f90d5b..3379f986c 100644 --- a/doc/pub/week42/html/._week42-bs010.html +++ b/doc/pub/week42/html/._week42-bs010.html @@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source - + -Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders +Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks @@ -88,16 +88,8 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec26'), - ('A simple example', 2, None, '___sec27'), - ('Set up of an RNN', 2, None, '___sec28'), - ('Solving differential equations and eigenvalue problems with ' - 'RNNs', - 2, - None, - '___sec29'), - ('Long-Short Time Memory', 2, None, '___sec30'), - ('Autoencoders: Overarching view', 2, None, '___sec31'), - ('Simple examples of Autoencoders', 2, None, '___sec32')]} + ('Set up of an RNN', 2, None, '___sec27'), + ('A simple example', 2, None, '___sec28')]} end of tocinfo --> @@ -127,7 +119,7 @@ MathJax.Hub.Config({ - Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders + Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks
    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -222,7 +210,7 @@ matrices, typically 1 for each color dimension (Red, Green, Blue).
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    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -218,7 +206,7 @@ $$
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    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -224,7 +212,7 @@ single neuron in the first hidden layer.
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    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -224,7 +212,7 @@ fixed, and known as a 22
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    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -228,7 +216,7 @@ layer.
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    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -221,7 +209,7 @@ classification.
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    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -254,7 +242,7 @@ plt.show()
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    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -236,7 +224,7 @@ X_train, X_test, Y_train, Y_test = train_tes
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    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -241,7 +229,7 @@ lmbd_vals = np.
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    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -231,7 +219,7 @@ MathJax.Hub.Config({
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    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -244,8 +232,6 @@ plt.show()
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    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -229,9 +217,6 @@ train_images, test_images = train_images 28
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    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -229,10 +217,6 @@ plt.show()
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    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
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  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -230,11 +218,6 @@ You can see that the output of every Conv2D and MaxPooling2D layer is a 3D tenso
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    • diff --git a/doc/pub/week42/html/._week42-bs024.html b/doc/pub/week42/html/._week42-bs024.html index d34f2c663..a04528bc2 100644 --- a/doc/pub/week42/html/._week42-bs024.html +++ b/doc/pub/week42/html/._week42-bs024.html @@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source - + -Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders +Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks @@ -88,16 +88,8 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec26'), - ('A simple example', 2, None, '___sec27'), - ('Set up of an RNN', 2, None, '___sec28'), - ('Solving differential equations and eigenvalue problems with ' - 'RNNs', - 2, - None, - '___sec29'), - ('Long-Short Time Memory', 2, None, '___sec30'), - ('Autoencoders: Overarching view', 2, None, '___sec31'), - ('Simple examples of Autoencoders', 2, None, '___sec32')]} + ('Set up of an RNN', 2, None, '___sec27'), + ('A simple example', 2, None, '___sec28')]} end of tocinfo --> @@ -127,7 +119,7 @@ MathJax.Hub.Config({ - Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders + Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks
    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -228,10 +216,6 @@ As you can see, our (4, 4, 64) outputs were flattened into vectors of shape (102
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    • diff --git a/doc/pub/week42/html/week42-bs.html b/doc/pub/week42/html/week42-bs.html index 8db429738..80d4c12ae 100644 --- a/doc/pub/week42/html/week42-bs.html +++ b/doc/pub/week42/html/week42-bs.html @@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source - + -Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders +Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks @@ -88,16 +88,8 @@ Automatically generated HTML file from DocOnce source 2, None, '___sec26'), - ('A simple example', 2, None, '___sec27'), - ('Set up of an RNN', 2, None, '___sec28'), - ('Solving differential equations and eigenvalue problems with ' - 'RNNs', - 2, - None, - '___sec29'), - ('Long-Short Time Memory', 2, None, '___sec30'), - ('Autoencoders: Overarching view', 2, None, '___sec31'), - ('Simple examples of Autoencoders', 2, None, '___sec32')]} + ('Set up of an RNN', 2, None, '___sec27'), + ('A simple example', 2, None, '___sec28')]} end of tocinfo --> @@ -127,7 +119,7 @@ MathJax.Hub.Config({ - Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders + Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks
    @@ -162,12 +154,8 @@ MathJax.Hub.Config({
  • Compile and train the model
  • Finally, evaluate the model
  • Recurrent neural networks: Overarching view
    • -
  • A simple example
    • -
  • Set up of an RNN
    • -
  • Solving differential equations and eigenvalue problems with RNNs
    • -
  • Long-Short Time Memory
    • -
  • Autoencoders: Overarching view
    • -
  • Simple examples of Autoencoders
    • +
  • Set up of an RNN
    • +
  • A simple example
    • @@ -186,7 +174,7 @@ MathJax.Hub.Config({
    -

    Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders

    +

    Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks

    @@ -202,7 +190,7 @@ MathJax.Hub.Config({

    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

    -

    Oct 15, 2020

    +

    Oct 16, 2020


    @@ -226,7 +214,7 @@ MathJax.Hub.Config({

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    • diff --git a/doc/pub/week42/html/week42-reveal.html b/doc/pub/week42/html/week42-reveal.html index 18465ad78..3e2338e10 100644 --- a/doc/pub/week42/html/week42-reveal.html +++ b/doc/pub/week42/html/week42-reveal.html @@ -3,9 +3,9 @@ - + -Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders +Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks @@ -132,7 +132,7 @@ MathJax.Hub.Config({ -

    Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders

    +

    Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks

    @@ -148,7 +148,7 @@ MathJax.Hub.Config({

    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

     
    -

    Oct 15, 2020

    +

    Oct 16, 2020


    @@ -163,7 +163,7 @@ MathJax.Hub.Config({

    • Thursday: Convolutional Neural Networks and examples. Video of Lecture
      • -

    • Friday: Recurrent Neural Networks and Autoencoders
      • +

    • Friday: Recurrent Neural Networks. Video of Lecture

    @@ -851,7 +851,18 @@ systems such as automatic translation and speech-to-text.

    -

    A simple example

    +

    Set up of an RNN

    + +

    +See the handwritten notes and the video from the lecture of October 16. + +

    +More text will be added later. +

    + + +
    +

    A simple example

    @@ -928,88 +939,6 @@ plt.show()

    -
    -

    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. -

    - - -
    -

    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. -

    - - -
    -

    Long-Short Time Memory

    - -

    -Discussions about dynamic unrolling through time. discuss memory cells, input and output -

    - - -
    -

    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. -

    - - -
    -

    Simple examples of Autoencoders

    -
    - -
    diff --git a/doc/pub/week42/html/week42-solarized.html b/doc/pub/week42/html/week42-solarized.html index cc13da454..15acaea3a 100644 --- a/doc/pub/week42/html/week42-solarized.html +++ b/doc/pub/week42/html/week42-solarized.html @@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source - + -Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders +Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks @@ -108,16 +108,8 @@ div { text-align: justify; text-justify: inter-word; } 2, None, '___sec26'), - ('A simple example', 2, None, '___sec27'), - ('Set up of an RNN', 2, None, '___sec28'), - ('Solving differential equations and eigenvalue problems with ' - 'RNNs', - 2, - None, - '___sec29'), - ('Long-Short Time Memory', 2, None, '___sec30'), - ('Autoencoders: Overarching view', 2, None, '___sec31'), - ('Simple examples of Autoencoders', 2, None, '___sec32')]} + ('Set up of an RNN', 2, None, '___sec27'), + ('A simple example', 2, None, '___sec28')]} end of tocinfo --> @@ -143,7 +135,7 @@ MathJax.Hub.Config({ -

    Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders

    +

    Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks

    @@ -159,7 +151,7 @@ MathJax.Hub.Config({

    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

    -

    Oct 15, 2020

    +

    Oct 16, 2020












    @@ -168,7 +160,7 @@ MathJax.Hub.Config({

    Reading suggestions for both days: Aurelien Geron's chapters 13 and 14. Autoencoders are discussed in chapter 15 of Geron's text. @@ -843,7 +835,18 @@ systems such as automatic translation and speech-to-text.











    -

    A simple example

    +

    Set up of an RNN

    + +

    +See the handwritten notes and the video from the lecture of October 16. + +

    +More text will be added later. + +

    +









    + +

    A simple example

    @@ -917,88 +920,6 @@ plt.plot(index,predicted) plt.axvline(df.index[Tp], c="r") plt.show()

    -

    -









    - -

    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. - -

    -









    - -

    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. - -

    -









    - -

    Long-Short Time Memory

    - -

    -Discussions about dynamic unrolling through time. discuss memory cells, input and output - -

    -









    - -

    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. - -

    -









    - -

    Simple examples of Autoencoders

    -

    diff --git a/doc/pub/week42/html/week42.html b/doc/pub/week42/html/week42.html index 2d7df1280..ff3687cb1 100644 --- a/doc/pub/week42/html/week42.html +++ b/doc/pub/week42/html/week42.html @@ -7,9 +7,9 @@ Automatically generated HTML file from DocOnce source - + -Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks and Autoencoders +Week 42 Convolutional (CNN) and Recurrent (RNN) Neural Networks