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+# 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
+
+- [keras.io](http://keras.io/) - Keras documentation
+- [Getting started with the Sequential model](http://keras.io/getting-started/sequential-model-guide/)
+- [Getting started with the functional API](http://keras.io/getting-started/functional-api-guide/)
+- [Keras FAQ](http://keras.io/getting-started/faq/)
+
+## Tutorials
+
+- [Using pre-trained word embeddings in a Keras model](http://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html)
+- [Building powerful image classification models using very little data](http://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html)
+- [Building Autoencoders in Keras](http://blog.keras.io/building-autoencoders-in-keras.html)
+- [A complete guide to using Keras as part of a TensorFlow workflow](http://blog.keras.io/keras-as-a-simplified-interface-to-tensorflow-tutorial.html)
+- Introduction to Keras, from University of Waterloo: [video](https://www.youtube.com/watch?v=Tp3SaRbql4k) [slides](https://uwaterloo.ca/data-science/sites/ca.data-science/files/uploads/files/keras_tutorial.pdf)
+- Introduction to Deep Learning with Keras, from CERN: [video](http://cds.cern.ch/record/2157570?ln=en) [slides](https://indico.cern.ch/event/506145/contributions/2132944/attachments/1258124/1858154/NNinKeras_MPaganini.pdf)
+- [Installing Keras for deep learning](http://www.pyimagesearch.com/2016/07/18/installing-keras-for-deep-learning/)
+- [Develop Your First Neural Network in Python With Keras Step-By-Step](http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/)
+- [Understanding Stateful LSTM Recurrent Neural Networks in Python with Keras](http://machinelearningmastery.com/understanding-stateful-lstm-recurrent-neural-networks-python-keras/)
+- [Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras](http://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/)
+- [Keras video tutorials from Dan Van Boxel](https://www.youtube.com/playlist?list=PLFxrZqbLojdKuK7Lm6uamegEFGW2wki6P)
+- [Keras Deep Learning Tutorial for Kaggle 2nd Annual Data Science Bowl](https://github.com/jocicmarko/kaggle-dsb2-keras/)
+
+## Code examples
+
+### Working with text
+
+- [Reuters topic classification](https://github.com/fchollet/keras/blob/master/examples/reuters_mlp.py)
+- [LSTM on the IMDB dataset (text sentiment classification)](https://github.com/fchollet/keras/blob/master/examples/imdb_lstm.py)
+- [Bidirectional LSTM on the IMDB dataset](https://github.com/fchollet/keras/blob/master/examples/imdb_bidirectional_lstm.py)
+- [1D CNN on the IMDB dataset](https://github.com/fchollet/keras/blob/master/examples/imdb_cnn.py)
+- [1D CNN-LSTM on the IMDB dataset](https://github.com/fchollet/keras/blob/master/examples/imdb_cnn_lstm.py)
+- [LSTM-based network on the bAbI dataset](https://github.com/fchollet/keras/blob/master/examples/babi_rnn.py)
+- [Memory network on the bAbI dataset (reading comprehension question answering)](https://github.com/fchollet/keras/blob/master/examples/babi_memnn.py)
+- [Sequence to sequence learning for performing additions of strings of digits](https://github.com/fchollet/keras/blob/master/examples/addition_rnn.py)
+- [LSTM text generation](https://github.com/fchollet/keras/blob/master/examples/lstm_text_generation.py)
+- [Using pre-trained word embeddings](https://github.com/fchollet/keras/blob/master/examples/pretrained_word_embeddings.py)
+
+### Working with images
+
+- [Simple CNN on MNST](https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py)
+- [Simple CNN on CIFAR10 with data augmentation](https://github.com/fchollet/keras/blob/master/examples/cifar10_cnn.py)
+- [Inception v3](https://github.com/fchollet/keras/blob/master/examples/inception_v3.py)
+- [VGG 16 (with pre-trained weights)](https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3)
+- [VGG 19 (with pre-trained weights)](https://gist.github.com/baraldilorenzo/8d096f48a1be4a2d660d)
+- ResNet 50 (with pre-trained weights): [1](https://github.com/fchollet/keras/pull/3266/files) [2](https://github.com/raghakot/keras-resnet)
+- [FractalNet](https://github.com/snf/keras-fractalnet)
+- [AlexNet, VGG 16, VGG 19, and class heatmap visualization](https://github.com/heuritech/convnets-keras)
+- [Visual-Semantic Embedding](https://github.com/awentzonline/keras-visual-semantic-embedding)
+- Variational Autoencoder: [with deconvolutions](https://github.com/fchollet/keras/blob/master/examples/variational_autoencoder_deconv.py) [with upsampling](https://github.com/fchollet/keras/blob/master/examples/variational_autoencoder.py)
+- [Visual question answering](https://github.com/avisingh599/visual-qa)
+- [Deep Networks with Stochastic Depth](https://github.com/dblN/stochastic_depth_keras)
+- [Smile detection with a CNN](https://github.com/kylemcdonald/SmileCNN)
+- [VGG-CAM](https://github.com/tdeboissiere/VGG16CAM-keras)
+
+
+### Creative visual applications
+
+- [Real-time style transfer](https://github.com/awentzonline/keras-rtst)
+- Style transfer: [1](https://github.com/fchollet/keras/blob/master/examples/neural_style_transfer.py) [2](https://github.com/titu1994/Neural-Style-Transfer)
+- [Image analogies](https://github.com/awentzonline/image-analogies): Generate image analogies using neural matching and blending.
+- [Visualizing the filters learned by a CNN](https://github.com/fchollet/keras/blob/master/examples/conv_filter_visualization.py)
+- [Deep dreams](https://github.com/fchollet/keras/blob/master/examples/deep_dream.py)
+- GAN / DCGAN: [1](https://github.com/phreeza/keras-GAN) [2](https://github.com/jacobgil/keras-dcgan) [3](https://github.com/osh/KerasGAN)
+
+### Reinforcement learning
+
+- [DQN](https://github.com/sherjilozair/dqn)
+- [FlappyBird DQN](https://github.com/yanpanlau/Keras-FlappyBird)
+- [async-RL](https://github.com/coreylynch/async-rl): Tensorflow + Keras + OpenAI Gym implementation of 1-step Q Learning from "Asynchronous Methods for Deep Reinforcement Learning"
+
+### Miscallenous architecture blueprints
+
+- [Stateful LSTM](https://github.com/fchollet/keras/blob/master/examples/stateful_lstm.py)
+- [Siamese network](https://github.com/fchollet/keras/blob/master/examples/mnist_siamese_graph.py)
+- [Pretraining on a different dataset](https://github.com/fchollet/keras/blob/master/examples/mnist_transfer_cnn.py)
+- [Neural programmer-interpreter](https://github.com/mokemokechicken/keras_npi)
+
+## Third-party libraries
+
+- [Elephas](https://github.com/maxpumperla/elephas): Distributed Deep Learning with Keras & Spark
+- [Hyperas](https://github.com/maxpumperla/hyperas): Hyperparameter optimization
+- [Hera](https://github.com/jakebian/hera): in-browser metrics dashboard for Keras models
+- [Kerlym](https://github.com/osh/kerlym): reinforcement learning with Keras and OpenAI Gym
+- [Qlearning4K](https://github.com/farizrahman4u/qlearning4k): reinforcement learning add-on for Keras
+- [seq2seq](https://github.com/farizrahman4u/seq2seq): Sequence to Sequence Learning with Keras
+- [Seya](https://github.com/EderSantana/seya): Keras extras
+- [Keras Language Modeling](https://github.com/codekansas/keras-language-modeling): Language modeling tools for Keras
+
+
+## Projects built with Keras
+
+- [RocAlphaGo](https://github.com/Rochester-NRT/RocAlphaGo): An independent, student-led replication of DeepMind's 2016 Nature publication, "Mastering the game of Go with deep neural networks and tree search"
+- [DeepJazz](https://github.com/jisungk/deepjazz): Deep learning driven jazz generation using Keras
+- [dataset-sts](https://github.com/brmson/dataset-sts): Semantic Text Similarity Dataset Hub