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 - Keras documentation
• Getting started with the Sequential model
• Getting started with the functional API
• Keras FAQ

Tutorials

• Quick start: the Iris dataset in Keras and scikit-learn
• Using pre-trained word embeddings in a Keras model
• Building powerful image classification models using very little data
• Building Autoencoders in Keras
• A complete guide to using Keras as part of a TensorFlow workflow
• Introduction to Keras, from University of Waterloo: video - slides
• Introduction to Deep Learning with Keras, from CERN: video - slides
• Installing Keras for deep learning
• Develop Your First Neural Network in Python With Keras Step-By-Step
• Practical Neural Networks with Keras: Classifying Yelp Reviews (Shows basic classification and how to set up a GPU instance on AWS)
• Understanding Stateful LSTM Recurrent Neural Networks in Python with Keras
• Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras
• Keras video tutorials from Dan Van Boxel
• Keras Deep Learning Tutorial for Kaggle 2nd Annual Data Science Bowl
• Collection of tutorials setting up DNNs with Keras
• Fast.AI - Practical Deep Learning For Coders, Part 1 (great information on deep learning in general, heavily uses Keras for the labs)
• Keras Tutorial: Content Based Image Retrieval Using a Convolutional Denoising Autoencoder
• A Bit of Deep Learning and Keras: a multipart video introduction to deep learning and keras
• Five simple examples of the Keras Functional API
• Keras video tutorials playlist from Deeplizard

Books based on Keras

• Deep Learning with Python
• Deep Learning with Keras
• Deep Learning and the Game of Go (MEAP)

Code examples

Working with text

• Reuters topic classification
• LSTM on the IMDB dataset (text sentiment classification)
• Bidirectional LSTM on the IMDB dataset
• 1D CNN on the IMDB dataset
• 1D CNN-LSTM on the IMDB dataset
• LSTM-based network on the bAbI dataset
• Memory network on the bAbI dataset (reading comprehension question answering)
• Sequence to sequence learning for performing additions of strings of digits
• LSTM text generation
• Using pre-trained word embeddings
• Monolingual and Multilingual Image Captioning
• FastText on the IMDB dataset
• Structurally constrained recurrent nets text generation
• Character-level convolutional neural nets for text classification
• LSTM to predict gender of a name
• Language/dialect identification with multiple character-level CNNs

Working with images

• Simple CNN on MNIST
• Simple CNN on CIFAR10 with data augmentation
• Inception v3
• VGG 16 (with pre-trained weights)
• VGG 19 (with pre-trained weights)
• ResNet 50 (with pre-trained weights): 1 - 2
• FractalNet
• AlexNet, VGG 16, VGG 19, and class heatmap visualization
• Visual-Semantic Embedding
• Variational Autoencoder: with deconvolutions - with upsampling
• Visual question answering
• Deep Networks with Stochastic Depth
• Smile detection with a CNN
• VGG-CAM
• t-SNE of image CNN fc7 activations
• VGG16 Deconvolution network
• Wide Residual Networks (with pre-trained weights): 1 - 2
• Ultrasound nerve segmentation: 1 - 2
• DeepMask object segmentation
• Densely Connected Convolutional Networks: 1 - 2
• Snapshot Ensembles: Train 1, Get M for Free
• Single Shot MultiBox Detector
• Popular Image Segmentation Models : FCN, Segnet, U-Net etc.

Creative visual applications

• Real-time style transfer
• Style transfer: 1 - 2
• Image analogies: Generate image analogies using neural matching and blending.
• Visualizing the filters learned by a CNN
• Deep dreams
• GAN / DCGAN: 1 - 2 - 3 - 4
• InfoGAN
• pix2pix
• DFI: Deep Feature Interpolation
• Colorful Image colorization: B&W to color

Reinforcement learning

• DQN
• FlappyBird DQN
• async-RL: Ten