diff --git a/doc/pub/week41/html/week41-bs.html b/doc/pub/week41/html/week41-bs.html index abd36d462..5f2dc230a 100644 --- a/doc/pub/week41/html/week41-bs.html +++ b/doc/pub/week41/html/week41-bs.html @@ -42,6 +42,10 @@ Automatically generated HTML file from DocOnce source
  • Plan for week 41
    • -
  • Setting up the Back propagation algorithm
    • -
  • Setting up a Multi-layer perceptron model for classification
    • -
  • Defining the cost function
    • -
  • Example: binary classification problem
    • -
  • The Softmax function
    • -
  • Developing a code for doing neural networks with back propagation
    • -
  • Collect and pre-process data
    • -
  • Train and test datasets
    • -
  • Define model and architecture
    • -
  • Layers
    • -
  • Weights and biases
    • -
  • Feed-forward pass
    • -
  • Matrix multiplications
    • -
  • Choose cost function and optimizer
    • -
  • Optimizing the cost function
    • -
  • Regularization
    • -
  • Matrix multiplication
    • -
  • Improving performance
    • -
  • Full object-oriented implementation
    • -
  • Evaluate model performance on test data
    • -
  • Adjust hyperparameters
    • -
  • Visualization
    • -
  • scikit-learn implementation
    • -
  • Visualization
    • -
  • Building neural networks in Tensorflow and Keras
    • -
  • Tensorflow
    • -
  • Using Keras
    • -
  • Collect and pre-process data
    • -
  • The Breast Cancer Data, now with Keras
    • -
  • Fine-tuning neural network hyperparameters
    • -
  • Hidden layers
    • -
  • Which activation function should I use?
    • -
  • Is the Logistic activation function (Sigmoid) our choice?
    • -
  • The derivative of the Logistic funtion
    • -
  • The RELU function family
    • -
  • Which activation function should we use?
    • -
  • More on activation functions, output layers
    • -
  • Batch Normalization
    • -
  • Dropout
    • -
  • Gradient Clipping
    • -
  • A very nice website on Neural Networks
    • -
  • A top-down perspective on Neural networks
    • -
  • Limitations of supervised learning with deep networks
    • -
  • Convolutional Neural Networks (recognizing images)
    • -
  • Regular NNs don’t scale well to full images
    • -
  • 3D volumes of neurons
    • -
  • Layers used to build CNNs
    • -
  • Transforming images
    • -
  • CNNs in brief
    • -
  • CNNs in more detail, building convolutional neural networks in Tensorflow and Keras
    • -
  • Setting it up
    • -
  • The MNIST dataset again
    • -
  • Strong correlations
    • -
  • Layers of a CNN
    • -
  • Systematic reduction
    • -
  • Prerequisites: Collect and pre-process data
    • -
  • Importing Keras and Tensorflow
    • -
  • Running with Keras
    • -
  • Final part
    • -
  • Final visualization
    • -
  • Fun links
    • +
  • Videos on Neural Networks
    • +
  • Setting up the Back propagation algorithm
    • +
  • Setting up a Multi-layer perceptron model for classification
    • +
  • Defining the cost function
    • +
  • Example: binary classification problem
    • +
  • The Softmax function
    • +
  • Developing a code for doing neural networks with back propagation
    • +
  • Collect and pre-process data
    • +
  • Train and test datasets
    • +
  • Define model and architecture
    • +
  • Layers
    • +
  • Weights and biases
    • +
  • Feed-forward pass
    • +
  • Matrix multiplications
    • +
  • Choose cost function and optimizer
    • +
  • Optimizing the cost function
    • +
  • Regularization
    • +
  • Matrix multiplication
    • +
  • Improving performance
    • +
  • Full object-oriented implementation
    • +
  • Evaluate model performance on test data
    • +
  • Adjust hyperparameters
    • +
  • Visualization
    • +
  • scikit-learn implementation
    • +
  • Visualization
    • +
  • Building neural networks in Tensorflow and Keras
    • +
  • Tensorflow
    • +
  • Using Keras
    • +
  • Collect and pre-process data
    • +
  • The Breast Cancer Data, now with Keras
    • +
  • Fine-tuning neural network hyperparameters
    • +
  • Hidden layers
    • +
  • Which activation function should I use?
    • +
  • Is the Logistic activation function (Sigmoid) our choice?
    • +
  • The derivative of the Logistic funtion
    • +
  • The RELU function family
    • +
  • Which activation function should we use?
    • +
  • More on activation functions, output layers
    • +
  • Batch Normalization
    • +
  • Dropout
    • +
  • Gradient Clipping
    • +
  • A very nice website on Neural Networks
    • +
  • A top-down perspective on Neural networks
    • +
  • Limitations of supervised learning with deep networks
    • +
  • Convolutional Neural Networks (recognizing images)
    • +
  • Regular NNs don’t scale well to full images
    • +
  • 3D volumes of neurons
    • +
  • Layers used to build CNNs
    • +
  • Transforming images
    • +
  • CNNs in brief
    • +
  • CNNs in more detail, building convolutional neural networks in Tensorflow and Keras
    • +
  • Setting it up
    • +
  • The MNIST dataset again
    • +
  • Strong correlations
    • +
  • Layers of a CNN
    • +
  • Systematic reduction
    • +
  • Prerequisites: Collect and pre-process data
    • +
  • Importing Keras and Tensorflow
    • +
  • Running with Keras
    • +
  • Final part
    • +
  • Final visualization
    • +
  • Fun links
    • @@ -328,7 +333,7 @@ MathJax.Hub.Config({
    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

    -

    Oct 9, 2021

    +

    Oct 11, 2021


    @@ -352,7 +357,7 @@ MathJax.Hub.Config({

  • 9
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  • »
    • diff --git a/doc/pub/week41/html/week41-reveal.html b/doc/pub/week41/html/week41-reveal.html index 30cbb8176..e4f976ffc 100644 --- a/doc/pub/week41/html/week41-reveal.html +++ b/doc/pub/week41/html/week41-reveal.html @@ -148,7 +148,7 @@ MathJax.Hub.Config({
    [2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University

     
    -

    Oct 9, 2021

    +

    Oct 11, 2021


    @@ -162,14 +162,24 @@ MathJax.Hub.Config({

    Plan for week 41

    Reading suggestions for both days: Aurelien Geron's chapters 10-11. -For amore in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications +For a more in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications. We strongly recommend reading these chapters from Goodfellow et on Deep Learning, that is chapters 6-12. Bishop's chapter 5 on Neural Networks is an additional good read. + + + +

    +

    Videos on Neural Networks

    + +
    diff --git a/doc/pub/week41/html/week41-solarized.html b/doc/pub/week41/html/week41-solarized.html index d49686ea2..2c58ec1d8 100644 --- a/doc/pub/week41/html/week41-solarized.html +++ b/doc/pub/week41/html/week41-solarized.html @@ -62,6 +62,10 @@ div { text-align: justify; text-justify: inter-word; } +

    Oct 11, 2021












    @@ -264,17 +268,26 @@ MathJax.Hub.Config({

    Plan for week 41

    Reading suggestions for both days: Aurelien Geron's chapters 10-11. -For amore in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications +For a more in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications. We strongly recommend reading these chapters from Goodfellow et on Deep Learning, that is chapters 6-12. Bishop's chapter 5 on Neural Networks is an additional good read.











    +

    Videos on Neural Networks

    + + + +









    +

    Setting up the Back propagation algorithm

    diff --git a/doc/pub/week41/html/week41.html b/doc/pub/week41/html/week41.html index 49e0c5776..d676fed29 100644 --- a/doc/pub/week41/html/week41.html +++ b/doc/pub/week41/html/week41.html @@ -67,6 +67,10 @@ div { text-align: justify; text-justify: inter-word; } +

    Oct 11, 2021












    @@ -269,17 +273,26 @@ MathJax.Hub.Config({

    Plan for week 41

    Reading suggestions for both days: Aurelien Geron's chapters 10-11. -For amore in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications +For a more in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications. We strongly recommend reading these chapters from Goodfellow et on Deep Learning, that is chapters 6-12. Bishop's chapter 5 on Neural Networks is an additional good read.











    +

    Videos on Neural Networks

    + + + +









    +

    Setting up the Back propagation algorithm

    diff --git a/doc/pub/week41/ipynb/ipynb-week41-src.tar.gz b/doc/pub/week41/ipynb/ipynb-week41-src.tar.gz index 60560e535..0eecbf512 100644 Binary files a/doc/pub/week41/ipynb/ipynb-week41-src.tar.gz and b/doc/pub/week41/ipynb/ipynb-week41-src.tar.gz differ diff --git a/doc/pub/week41/ipynb/week41.ipynb b/doc/pub/week41/ipynb/week41.ipynb index 4ed69faf1..763a6c2c6 100644 --- a/doc/pub/week41/ipynb/week41.ipynb +++ b/doc/pub/week41/ipynb/week41.ipynb @@ -10,7 +10,7 @@ " \n", "**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n", "\n", - "Date: **Oct 9, 2021**\n", + "Date: **Oct 11, 2021**\n", "\n", "Copyright 1999-2021, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n", "\n", @@ -19,13 +19,19 @@ "\n", "## Plan for week 41\n", "\n", - "* Thursday: Building our own Feed-forward Neural Network. \n", + "* Thursday: Building our own Feed-forward Neural Network and discussion of project 2.\n", "\n", "* Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Start convolutional Neural Networks (CNN). \n", "\n", "Reading suggestions for both days: [Aurelien Geron's chapters 10-11](https://github.com/CompPhysics/MachineLearning/blob/master/doc/T\\\n", "extbooks/TensorflowML.pdf).\n", - "For amore in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications\n", + "For a more in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications. We strongly recommend reading these chapters from Goodfellow et on Deep Learning, that is chapters 6-12. Bishop's chapter 5 on Neural Networks is an additional good read.\n", + "\n", + "## Videos on Neural Networks\n", + "\n", + "* [Video on Neural Networks](https://www.youtube.com/watch?v=CqOfi41LfDw)\n", + "\n", + "* [Video on the back propagation algorithm](https://www.youtube.com/watch?v=Ilg3gGewQ5U)\n", "\n", "## Setting up the Back propagation algorithm\n", "\n", diff --git a/doc/src/week41/week41.do.txt b/doc/src/week41/week41.do.txt index 0f6eed14c..2b7976610 100644 --- a/doc/src/week41/week41.do.txt +++ b/doc/src/week41/week41.do.txt @@ -6,12 +6,20 @@ DATE: today !split ===== Plan for week 41 ===== -* Thursday: Building our own Feed-forward Neural Network. +* Thursday: Building our own Feed-forward Neural Network and discussion of project 2. * Friday: Playing around with our own Feed-forward Neural Network and introduction to TensorFlow. Start convolutional Neural Networks (CNN). Reading suggestions for both days: "Aurelien Geron's chapters 10-11":"https://github.com/CompPhysics/MachineLearning/blob/master/doc/T\ extbooks/TensorflowML.pdf". -For amore in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications +For a more in depth discussion on neural networks we recommend Goodfellow et al chapters 6 and 7. For CNNs, see Goodfellow et al chapter 9. chapter 11 and 12 on practicalities and applications. We strongly recommend reading these chapters from Goodfellow et on Deep Learning, that is chapters 6-12. Bishop's chapter 5 on Neural Networks is an additional good read. + +!split +===== Videos on Neural Networks ===== + +* "Video on Neural Networks":"https://www.youtube.com/watch?v=CqOfi41LfDw" + +* "Video on the back propagation algorithm":"https://www.youtube.com/watch?v=Ilg3gGewQ5U" + !split ===== Setting up the Back propagation algorithm =====