Keras resources

This is a directory of tutorials and open-source code repositories for working with Keras, the Python deep learning library.

If you have a high-quality tutorial or project to add, please open a PR.

Official starter resources

DQN
FlappyBird DQN
async-RL: Tensorflow + Keras + OpenAI Gym implementation of 1-step Q Learning from "Asynchronous Methods for Deep Reinforcement Learning"
keras-rl: A library for state-of-the-art reinforcement learning. Integrates with OpenAI Gym and implements DQN, double DQN, Continuous DQN, and DDPG.

Elephas: Distributed Deep Learning with Keras & Spark
Hyperas: Hyperparameter optimization
Hera: in-browser metrics dashboard for Keras models
Kerlym: reinforcement learning with Keras and OpenAI Gym
Qlearning4K: reinforcement learning add-on for Keras
seq2seq: Sequence to Sequence Learning with Keras
Seya: Keras extras
Keras Language Modeling: Language modeling tools for Keras
Recurrent Shop: Framework for building complex recurrent neural networks with Keras
Keras.js: Run trained Keras models in the browser, with GPU support
keras-vis: Neural network visualization toolkit for keras.

RocAlphaGo: An independent, student-led replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search"
BetaGo: Deep Learning Go bots using Keras
DeepJazz: Deep learning driven jazz generation using Keras
dataset-sts: Semantic Text Similarity Dataset Hub
snli-entailment: Independent implementation of attention model for textual entailment from the paper "Reasoning about Entailment with Neural Attention".
Headline generator: independent implementation of Generating News Headlines with Recurrent Neural Networks
LipNet: independent implementation of LipNet: End-to-End Sentence-level Lipreading.
caption_generator: An implementation of image caption generation in natural language inspired from Show and Tell: A Neural Image Caption Generator.
NMT-Keras: Neural Machine Translation using Keras.
Conx - easy-to-use layer on top of Keras, with visualizations (eg, no knowledge of numpy needed)