/Deep-Learning-modules-in-Keras

A project about the brief implementation of MLP, CNN, RNN, Unsupervised learning with Autoencoders, ETC, Text analytics using CNN in Python

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Deep-Learning-modules-in-Keras

A project about the brief implementation of MLP, CNN, RNN, Unsupervised learning with Autoencoders, ETC, Text analytics using CNN in Python

1. Multilayer Perceptrons

1) Basics of MLP

  • Regression tasks with MLP
  • Classification tasks with MLP

2) Advanced MLP - 1

  • Weight initialization schemes
  • Activation functions (Nonlinearity)
  • Batch normalization
  • Optimizers
  • Dropout
  • Ensemble of models

3) Advanced MLP - 2

  • Putting it altogether

2. Convolutional Neural Networks

1) Basic CNN

  • Basics of CNN architecture

2) Advanced CNN - 1

  • Getting deeper with CNNs

3) Advanced CNN - 2

  • CNN for sentence classification (imdb)

4) Using pretrained models

  • Importing models already trained on ImageNet dataset (keras.applications)

3. Recurrent Neural Networks

1) Basic RNN

  • Understanding RNN architecture
  • Vanilla RNN (SimpleRNN)
  • Stacked vanilla RNN
  • LSTM
  • Stacked LSTM

2) Advanced RNN - 1

  • Deep RNNs
  • Bidirectional RNNs
  • Deep bidirectional RNNs

3) Advanced RNN - 2

  • CNN-RNN

4) Advanced RNN - 3

  • CuDNN LSTM
  • CuDNN GRU

4. Unsupervised Learning

1) Autoencoders

  • Autoencoder basics
  • Convolutional autoencoder
  • Dimensionality reduction using autoencoder

5. ETC

0) Creating models

  • Sequential API
  • Model Functional API

1) Image processing

  • Importing images

2) Keras callbacks

  • ModelCheckpoint
  • EarlyStopping
  • ReduceLROnPlateau

3) Using GPUs

  • Make your training process faster with CUDA & CuDNN

4) Model selection

  • Cross validation
  • Grid search
  • Random search

5) Class weighted learning

  • Learning under class imbalance situations

6) Model weights

  • Getting model weights
  • Loading & saving model weights

6. Examples

1) Digit Recognition with RNN

  • Simple RNN model
  • Stacked RNN model
  • Bidirectional RNN model
  • Simple LSTM model
  • Stacked LSTM model
  • Bidirectional LSTM model
  • Simple GRU model
  • Stacked GRU model
  • Bidirectional GRU model

2) Fashion item classification with MLP

  • Simple MLP
  • Autoencoder + MLP (dimensionality reduction)

3) Question generation with seq2seq (using Quora dataset)

 - Generating similar questions with seq2seq model

4) CNN for sentence classification

  • CNN-static implementation of Kim 2014 paper

5) Sentiment Analysis in Korean (using Naver Sentiment Movie Corpus)

  • Sentiment analysis with Logistic Regression (using sklearn & TF)
  • Sentiment analysis with RNN

7. Text Analytics

Section with emphasis on text data analytics

1) Text processing

2) Word embedding

3) CNNs for text data

  • 1-D Convolution for text analysis
  • CNN for setnence classification (Kim 2014)
  • Dynamic CNN for sentence modeling (Kalchbrenner et al 2014)
  • CNN for text categorization (Johnson and Zhang 2014)