{"id":13444484,"url":"https://github.com/fchollet/keras-resources","last_synced_at":"2025-04-10T06:16:43.291Z","repository":{"id":142735092,"uuid":"64428695","full_name":"fchollet/keras-resources","owner":"fchollet","description":"Directory of tutorials and open-source code repositories for working with Keras, the Python deep learning library","archived":false,"fork":false,"pushed_at":"2024-02-12T09:00:44.000Z","size":37,"stargazers_count":3250,"open_issues_count":17,"forks_count":886,"subscribers_count":214,"default_branch":"master","last_synced_at":"2025-04-03T04:04:57.829Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/fchollet.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-07-28T21:15:05.000Z","updated_at":"2025-03-28T17:43:07.000Z","dependencies_parsed_at":"2025-03-27T03:43:42.959Z","dependency_job_id":"c2de44f6-f273-42f3-9efb-c0c89b00af54","html_url":"https://github.com/fchollet/keras-resources","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fchollet%2Fkeras-resources","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fchollet%2Fkeras-resources/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fchollet%2Fkeras-resources/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/fchollet%2Fkeras-resources/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/fchollet","download_url":"https://codeload.github.com/fchollet/keras-resources/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248166856,"owners_count":21058481,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-07-31T04:00:24.928Z","updated_at":"2025-04-10T06:16:42.990Z","avatar_url":"https://github.com/fchollet.png","language":null,"funding_links":[],"categories":["Uncategorized","Others","Awesome lists"],"sub_categories":["Uncategorized","Frameworks / ecosystems"],"readme":"# Keras resources\n\nThis is a directory of tutorials and open-source code repositories for working with Keras, the Python deep learning library.\n\nIf you have a high-quality tutorial or project to add, please open a PR.\n\n## Official starter resources\n\n- [keras.io](http://keras.io/) - Keras documentation\n- [Getting started with the Sequential model](http://keras.io/getting-started/sequential-model-guide/)\n- [Getting started with the functional API](http://keras.io/getting-started/functional-api-guide/)\n- [Keras FAQ](http://keras.io/getting-started/faq/)\n\n## Tutorials\n\n- [Quick start: the Iris dataset in Keras and scikit-learn](https://github.com/fastforwardlabs/keras-hello-world/blob/master/kerashelloworld.ipynb)\n- [Using pre-trained word embeddings in a Keras model](http://blog.keras.io/using-pre-trained-word-embeddings-in-a-keras-model.html)\n- [Building powerful image classification models using very little data](http://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html)\n- [Building Autoencoders in Keras](http://blog.keras.io/building-autoencoders-in-keras.html)\n- [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)\n- 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)\n- 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)\n- [Installing Keras for deep learning](http://www.pyimagesearch.com/2016/07/18/installing-keras-for-deep-learning/)\n- [Develop Your First Neural Network in Python With Keras Step-By-Step](http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/)\n- [Practical Neural Networks with Keras: Classifying Yelp Reviews](http://www.developintelligence.com/blog/2017/06/practical-neural-networks-keras-classifying-yelp-reviews/) (Shows basic classification and how to set up a GPU instance on AWS)\n- [Understanding Stateful LSTM Recurrent Neural Networks in Python with Keras](http://machinelearningmastery.com/understanding-stateful-lstm-recurrent-neural-networks-python-keras/)\n- [Time Series Prediction with LSTM Recurrent Neural Networks in Python with Keras](http://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/)\n- [Keras video tutorials from Dan Van Boxel](https://www.youtube.com/playlist?list=PLFxrZqbLojdKuK7Lm6uamegEFGW2wki6P)\n- [Keras Deep Learning Tutorial for Kaggle 2nd Annual Data Science Bowl](https://github.com/jocicmarko/kaggle-dsb2-keras/)\n- [Collection of tutorials setting up DNNs with Keras](http://ml4a.github.io/guides/)\n- [Fast.AI - Practical Deep Learning For Coders, Part 1](http://course.fast.ai/) (great information on deep learning in general, heavily uses Keras for the labs)\n- [Keras Tutorial: Content Based Image Retrieval Using a Convolutional Denoising Autoencoder](https://blog.sicara.com/keras-tutorial-content-based-image-retrieval-convolutional-denoising-autoencoder-dc91450cc511)\n- [A Bit of Deep Learning and Keras](https://www.youtube.com/watch?v=UOEhojCzWrY\u0026list=PLgJhDSE2ZLxaPX0jteHZG4skdj8ZrST9d): a multipart video introduction to deep learning and keras\n- [Five simple examples of the Keras Functional API](http://www.puzzlr.org/the-keras-functional-api-five-simple-examples/)\n- [Keras video tutorials playlist from Deeplizard](https://www.youtube.com/watch?v=RznKVRTFkBY\u0026list=PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL)\n\n## Books based on Keras\n\n- [Deep Learning with Python](https://www.amazon.com/Deep-Learning-Python-Francois-Chollet/dp/1617294438/)\n- [Deep Learning with Keras](https://www.amazon.com/Deep-Learning-Keras-Implementing-learning/dp/1787128423/)\n- [Deep Learning and the Game of Go (MEAP)](https://www.manning.com/books/deep-learning-and-the-game-of-go)\n\n## Code examples\n\n### Working with text\n\n- [Reuters topic classification](https://github.com/fchollet/keras/blob/master/examples/reuters_mlp.py)\n- [LSTM on the IMDB dataset (text sentiment classification)](https://github.com/fchollet/keras/blob/master/examples/imdb_lstm.py)\n- [Bidirectional LSTM on the IMDB dataset](https://github.com/fchollet/keras/blob/master/examples/imdb_bidirectional_lstm.py)\n- [1D CNN on the IMDB dataset](https://github.com/fchollet/keras/blob/master/examples/imdb_cnn.py)\n- [1D CNN-LSTM on the IMDB dataset](https://github.com/fchollet/keras/blob/master/examples/imdb_cnn_lstm.py)\n- [LSTM-based network on the bAbI dataset](https://github.com/fchollet/keras/blob/master/examples/babi_rnn.py)\n- [Memory network on the bAbI dataset (reading comprehension question answering)](https://github.com/fchollet/keras/blob/master/examples/babi_memnn.py)\n- [Sequence to sequence learning for performing additions of strings of digits](https://github.com/fchollet/keras/blob/master/examples/addition_rnn.py)\n- [LSTM text generation](https://github.com/fchollet/keras/blob/master/examples/lstm_text_generation.py)\n- [Using pre-trained word embeddings](https://github.com/fchollet/keras/blob/master/examples/pretrained_word_embeddings.py)\n- [Monolingual and Multilingual Image Captioning](https://github.com/elliottd/GroundedTranslation)\n- [FastText on the IMDB dataset](https://github.com/fchollet/keras/blob/master/examples/imdb_fasttext.py)\n- [Structurally constrained recurrent nets text generation](https://github.com/nzw0301/keras-examples/blob/master/SCRNLM.ipynb)\n- [Character-level convolutional neural nets for text classification](https://github.com/johnb30/py_crepe)\n- [LSTM to predict gender of a name](https://github.com/divamgupta/lstm-gender-predictor)\n- [Language/dialect identification with multiple character-level CNNs](https://github.com/boknilev/dsl-char-cnn)\n\n### Working with images\n\n- [Simple CNN on MNIST](https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py)\n- [Simple CNN on CIFAR10 with data augmentation](https://github.com/fchollet/keras/blob/master/examples/cifar10_cnn.py)\n- [Inception v3](https://github.com/fchollet/keras/blob/master/examples/inception_v3.py)\n- [VGG 16 (with pre-trained weights)](https://gist.github.com/baraldilorenzo/07d7802847aaad0a35d3)\n- [VGG 19 (with pre-trained weights)](https://gist.github.com/baraldilorenzo/8d096f48a1be4a2d660d)\n- ResNet 50 (with pre-trained weights): [1](https://github.com/fchollet/keras/pull/3266/files) - [2](https://github.com/raghakot/keras-resnet)\n- [FractalNet](https://github.com/snf/keras-fractalnet)\n- [AlexNet, VGG 16, VGG 19, and class heatmap visualization](https://github.com/heuritech/convnets-keras)\n- [Visual-Semantic Embedding](https://github.com/awentzonline/keras-visual-semantic-embedding)\n- 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)\n- [Visual question answering](https://github.com/avisingh599/visual-qa)\n- [Deep Networks with Stochastic Depth](https://github.com/dblN/stochastic_depth_keras)\n- [Smile detection with a CNN](https://github.com/kylemcdonald/SmileCNN)\n- [VGG-CAM](https://github.com/tdeboissiere/VGG16CAM-keras)\n- [t-SNE of image CNN fc7 activations](https://github.com/ml4a/ml4a-guides/blob/master/notebooks/tsne-images.ipynb)\n- [VGG16 Deconvolution network](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/DeconvNet)\n- Wide Residual Networks (with pre-trained weights): [1](https://github.com/asmith26/wide_resnets_keras) - [2](https://github.com/titu1994/Wide-Residual-Networks)\n- Ultrasound nerve segmentation: [1](https://github.com/jocicmarko/ultrasound-nerve-segmentation) - [2](https://github.com/raghakot/ultrasound-nerve-segmentation)\n- [DeepMask object segmentation](https://github.com/abbypa/NNProject_DeepMask)\n- Densely Connected Convolutional Networks: [1](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/DenseNet) - [2](https://github.com/titu1994/DenseNet)\n- [Snapshot Ensembles: Train 1, Get M for Free](https://github.com/titu1994/Snapshot-Ensembles)\n- [Single Shot MultiBox Detector](https://github.com/rykov8/ssd_keras)\n- [Popular Image Segmentation Models : FCN, Segnet, U-Net etc. ](https://github.com/divamgupta/image-segmentation-keras)\n\n### Creative visual applications\n\n- [Real-time style transfer](https://github.com/awentzonline/keras-rtst)\n- Style transfer: [1](https://github.com/fchollet/keras/blob/master/examples/neural_style_transfer.py) - [2](https://github.com/titu1994/Neural-Style-Transfer)\n- [Image analogies](https://github.com/awentzonline/image-analogies): Generate image analogies using neural matching and blending.\n- [Visualizing the filters learned by a CNN](https://github.com/fchollet/keras/blob/master/examples/conv_filter_visualization.py)\n- [Deep dreams](https://github.com/fchollet/keras/blob/master/examples/deep_dream.py)\n- GAN / DCGAN: [1](https://github.com/phreeza/keras-GAN) - [2](https://github.com/jacobgil/keras-dcgan) - [3](https://github.com/osh/KerasGAN) - [4](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/GAN)\n- [InfoGAN](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/InfoGAN)\n- [pix2pix](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/pix2pix)\n- [DFI](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/DFI): Deep Feature Interpolation\n- [Colorful Image colorization](https://github.com/tdeboissiere/DeepLearningImplementations/tree/master/Colorful): B\u0026W to color\n\n### Reinforcement learning\n\n- [DQN](https://github.com/sherjilozair/dqn)\n- [FlappyBird DQN](https://github.com/yanpanlau/Keras-FlappyBird)\n- [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\"\n- [keras-rl](https://github.com/matthiasplappert/keras-rl): A library for state-of-the-art reinforcement learning. Integrates with OpenAI Gym and implements DQN, double DQN, Continuous DQN, and DDPG.\n\n### Miscallenous architecture blueprints\n\n- [Stateful LSTM](https://github.com/fchollet/keras/blob/master/examples/stateful_lstm.py)\n- [Siamese network](https://github.com/fchollet/keras/blob/master/examples/mnist_siamese_graph.py)\n- [Pretraining on a different dataset](https://github.com/fchollet/keras/blob/master/examples/mnist_transfer_cnn.py)\n- [Neural programmer-interpreter](https://github.com/mokemokechicken/keras_npi)\n\n## Third-party libraries\n\n- [Elephas](https://github.com/maxpumperla/elephas): Distributed Deep Learning with Keras \u0026 Spark\n- [Hyperas](https://github.com/maxpumperla/hyperas): Hyperparameter optimization\n- [Hera](https://github.com/jakebian/hera): in-browser metrics dashboard for Keras models\n- [Kerlym](https://github.com/osh/kerlym): reinforcement learning with Keras and OpenAI Gym\n- [Qlearning4K](https://github.com/farizrahman4u/qlearning4k): reinforcement learning add-on for Keras\n- [seq2seq](https://github.com/farizrahman4u/seq2seq): Sequence to Sequence Learning with Keras\n- [Seya](https://github.com/EderSantana/seya): Keras extras\n- [Keras Language Modeling](https://github.com/codekansas/keras-language-modeling): Language modeling tools for Keras\n- [Recurrent Shop](https://github.com/datalogai/recurrentshop): Framework for building complex recurrent neural networks with Keras\n- [Keras.js](https://github.com/transcranial/keras-js): Run trained Keras models in the browser, with GPU support\n- [keras-vis](https://github.com/raghakot/keras-vis): Neural network visualization toolkit for keras.\n\n## Projects built with Keras\n\n- [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\"\n- [BetaGo](https://github.com/maxpumperla/betago): Deep Learning Go bots using Keras\n- [DeepJazz](https://github.com/jisungk/deepjazz): Deep learning driven jazz generation using Keras\n- [dataset-sts](https://github.com/brmson/dataset-sts): Semantic Text Similarity Dataset Hub\n- [snli-entailment](https://github.com/shyamupa/snli-entailment): Independent implementation of attention model for textual entailment from the paper [\"Reasoning about Entailment with Neural Attention\"](http://arxiv.org/abs/1509.06664).\n- [Headline generator](https://github.com/udibr/headlines): independent implementation of [Generating News Headlines with Recurrent Neural Networks](http://arxiv.org/abs/1512.01712)\n- [LipNet](https://github.com/rizkiarm/LipNet): independent implementation of [LipNet: End-to-End Sentence-level Lipreading](https://arxiv.org/abs/1611.01599).\n- [caption_generator](https://github.com/anuragmishracse/caption_generator): An implementation of image caption generation in natural language inspired from [Show and Tell: A Neural Image Caption Generator](https://arxiv.org/pdf/1411.4555.pdf).\n- [NMT-Keras](https://github.com/lvapeab/nmt-keras): Neural Machine Translation using Keras.\n- [Conx](https://conx.readthedocs.io/) - easy-to-use layer on top of Keras, with visualizations (eg, no knowledge of numpy needed)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffchollet%2Fkeras-resources","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffchollet%2Fkeras-resources","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffchollet%2Fkeras-resources/lists"}