Awesome Keras | Massive Resources & Collection ⭐

[SaM]

Awesome Keras

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: 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.

Miscallenous architecture blueprints

• Stateful LSTM
• Siamese network
• Pretraining on a different dataset
• Neural programmer-interpreter

Third-party libraries

• 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.

Projects built with 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)

Modeling Tools/Utilities

1. https://github.com/joeddav/devol
2. https://github.com/maxpumperla/hyperas
3. https://github.com/keras-team/keras-contrib
4. Accelerating Deep Learning with Multiprocess Image Augmentation in Keras (accompanying blog post)
5. ml-tools: Tools for common machine learning tasks using Tensorflow and Keras
6. https://github.com/kuza55/keras-extras
7. keras-multi-gpu: Multi-GPU data-parallel training in Keras
8. keras_callbacks_example: Keras Callback Examples

Implementation (from Scratch)/Pre-trained Models

1. https://github.com/raghakot/keras-resnet
2. https://github.com/XifengGuo/CapsNet-Keras
3. https://github.com/kentsommer/keras-inceptionV4
4. https://github.com/fchollet/deep-learning-models
5. https://github.com/titu1994/DenseNet
6. BatchRenormalization: Batch Renormalization algorithm implementation in Keras
7. mlp: Multilayer Perceptron Keras wrapper for sklearn
8. Image-Classification-Mobile: Sandbox for training large-scale image classification networks for embedded systems, including collection of pretrained classification models for Keras with MXNet backend

Visualization Tools

1. https://github.com/merantix/picasso
2. https://github.com/raghakot/keras-vis
3. https://github.com/fchollet/hualos
4. quiver: Interactive convnet features visualization for Keras (homepage)
5. hera: Train/evaluate a Keras model, get metrics streamed to a dashboard in your browser
6. picard: Easily declare large spaces of (keras) neural networks and run (hyperopt) optimization experiments on them (homepage)
7. keras-visualize-activations: Activation Maps Visualization for Keras

Recurrent Networks

1. recurrentshop: Framework for building complex recurrent neural networks with Keras

Tutorial

1. https://github.com/sachinruk/deepschool.io
2. https://github.com/leriomaggio/deep-learning-keras-tensorflow
3. https://github.com/kailashahirwar/cheatsheets-ai
4. https://github.com/donnemartin/data-science-ipython-notebooks
5. https://github.com/xingkongliang/Keras-Tutorials
6. https://github.com/anujgupta82/DeepNets/tree/master/Keras/Keras_from_scratch
7. https://github.com/chibuk/simple-cnn-keras-colaboratory

Art

1. https://github.com/OsciiArt/DeepAA
2. https://github.com/titu1994/Neural-Style-Transfer

Interoperating with other frameworks

1. https://github.com/Microsoft/MMdnn
2. model-converters: Tools for converting Keras models for use with other ML frameworks
3. PyTorch to Keras model converter
4. Gluon to Keras model converter

Source: GitHub

ENJOY & HAPPY LEARNING!

[Jared]

Great share @SaM. For some reason I can’t open the page you posted for the Pandas resources. All other pages load fine but when I try to open the Pandas post it just comes up as a blank page.

[SaM]

@Jared

I can’t open the page you posted for the Pandas resources.

I will post a solution shortly

EDITED:

Solution To Open Thread If You're Unable To Open It Or Unable To Visit It

Hello Folks Well, to keep the topic short let me add only basic ones. From the last few days, I noticed many members here having trouble accessing/visiting topics here, especially, Awesome resources and other topics where links get shared more than 20 times. Example: Causes behind the scene: This is a temp bug from the backend If you open a topic in a new tab, it won’t open properly, or if you click topic from the home page it probably won’t open. So all you have to do is to search …