Fundamentals of Deep Learning

This repository is the code companion to my book "Fundamentals of Deep Learning." All algorithms are implemented in Tensorflow, Google's new machine intelligence library.

TODO

Networks

• Logistic Regression (Nikhil)
• Multilayer Perceptron (Nikhil)
• Convolutional Network (Nikhil)
• Neural Style (Anish)
• Autoencoder (Hassan)
• Denoising Autoencoder (Hassan)
• Convolutional Autoencoder (Hassan)
• RNN (Nikhil)
• LSTM Network (Nikhil)
• GRU Network (Nikhil)
• LSTM + Attention (Nikhil)
• RCNN (Nikhil)
• Memory Networks (Nikhil)
• Pointer Networks
• Neural Turing Machines
• Neural Programmer
• DQN
• LSTM-DQN
• Deep Convolutional Inverse Graphics Network
• Highway Networks
• Deep Residual Networks

Embedding

• Word2Vec (Nikhil)
• Skip-gram/CBoW
• GloVe (Nikhil)
• Skip-thought Vectors (Nikhil)

Optimizers

• MLP + Momentum
• MLP + RMSProp
• MLP + ADAM
• MLP + FTRL
• MLP + ADADELTA