mirror of
https://github.com/donnemartin/data-science-ipython-notebooks.git
synced 2024-03-22 13:30:56 +08:00
67 lines
2.1 KiB
Markdown
67 lines
2.1 KiB
Markdown
|
# 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`).
|