Outline (Draft)

• Part I: Introduction

  • Intro to ANN

    • (naive pure-Python implementation from pybrain)
    • fast forward
    • sgd + backprop

  • Intro to Theano

    • Model + SGD with Theano (simple logreg)

  • Introduction to Keras

    • Overview and main features
      • Theano backend
      • Tensorflow backend
    • Same LogReg with Keras

• Part II: Supervised Learning + Keras Internals

  • Intro: Focus on Image Classification

  • Multi-Layer Perceptron and Fully Connected

    • Examples with keras.models.Sequential and Dense
    • HandsOn: MLP with keras

  • Intro to CNN

    • meaning of convolutional filters

      • examples from ImageNet

    • Meaning of dimensions of Conv filters (through an exmple of ConvNet)

    • HandsOn: ConvNet with keras

  • Advanced CNN

    • Dropout and MaxPooling

  • Famous ANN in Keras (likely moved somewhere else)

    • ref: https://github.com/fchollet/deep-learning-models
    • VGG16
    • VGG19
    • LaNet
    • Inception/GoogleNet
    • ResNet *Implementation and examples
    • HandsOn: Fine tuning a network on new dataset

• Part III: Unsupervised Learning + Keras Internals

  • AutoEncoders
  • word2vec & doc2vec (gensim) + keras.dataset (i.e. keras.dataset.imdb)
  • HandsOn: _______

*should we include embedding here?

• Part IV: Advanced Materials

  • RNN (LSTM)

    • RNN, LSTM, GRU
    • Meaning of dimensions of rnn (backprop though time, etc)
    • HandsOn: IMDB (?)

  • CNN-RNN

  • Time Distributed Convolution

  • Some of the recent advances in DL implemented in Keras

    • e.g. https://github.com/snf/keras-fractalnet - Fractal Net Implementation with Keras

Notes:

1. Please, add more details in Part IV (i.e. /Advanced Materials/)
2. As for Keras internals, I Would consider this: https://github.com/wuaalb/keras_extensions/blob/master/keras_extensions/rbm.py This is just to show how easy it is to extend Keras ( in this case, properly creating a new Layer).