A curated list of awesome TensorFlow experiments, libraries, and projects. Inspired by awesome-machine-learning.
TensorFlow is an open source software library for numerical computation using data flow graphs. In other words, the best way to build deep learning models.
More info here.
## Tutorials * [TensorFlow Tutorial 1](https://github.com/pkmital/tensorflow_tutorials) - From the basics to slightly more interesting applications of TensorFlow * [TensorFlow Tutorial 2](https://github.com/nlintz/TensorFlow-Tutorials) - Introduction to deep learning based on Google's TensorFlow framework. These tutorials are direct ports of Newmu's Theano * [TensorFlow Examples](https://github.com/aymericdamien/TensorFlow-Examples) - TensorFlow tutorials and code examples for beginners * [Sungjoon's TensorFlow-101](https://github.com/sjchoi86/Tensorflow-101) - TensorFlow tutorials written in Python with Jupyter Notebook * [Terry Um’s TensorFlow Exercises](https://github.com/terryum/TensorFlow_Exercises) - Re-create the codes from other TensorFlow examples * [Installing TensorFlow on Raspberry Pi 3](https://github.com/samjabrahams/tensorflow-on-raspberry-pi) - TensorFlow compiled and running properly on the Raspberry Pi * [Classification on time series](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition) - Recurrent Neural Network classification in Tensorflow with LSTM on cellphone sensor data ## Models/Projects * [Show, Attend and Tell] (https://github.com/yunjey/show_attend_and_tell) - Attention Based Image Caption Generator * [Pretty Tensor](https://github.com/google/prettytensor) - Pretty Tensor provides a high level builder API * [Neural Style](https://github.com/anishathalye/neural-style) - An implementation of neural style * [TensorFlow White Paper Notes](https://github.com/samjabrahams/tensorflow-white-paper-notes) - Annotated notes and summaries of the TensorFlow white paper, along with SVG figures and links to documentation * [NeuralArt](https://github.com/ckmarkoh/neuralart_tensorflow) - Implementation of A Neural Algorithm of Artistic Style * [Deep-Q learning Pong with TensorFlow and PyGame](http://www.danielslater.net/2016/03/deep-q-learning-pong-with-tensorflow.html) * [Generative Handwriting Demo using TensorFlow](https://github.com/hardmaru/write-rnn-tensorflow) - An attempt to implement the random handwriting generation portion of Alex Graves' paper * [Neural Turing Machine in TensorFlow](https://github.com/carpedm20/NTM-tensorflow) - implementation of Neural Turing Machine * [GoogleNet Convolutional Neural Network Groups Movie Scenes By Setting] (https://github.com/agermanidis/thingscoop) - Search, filter, and describe videos based on objects, places, and other things that appear in them * [Neural machine translation between the writings of Shakespeare and modern English using TensorFlow](https://github.com/tokestermw/tensorflow-shakespeare) - This performs a monolingual translation, going from modern English to Shakespeare and vis-versa. * [Chatbot](https://github.com/Conchylicultor/DeepQA) - Implementation of ["A neural conversational model"](http://arxiv.org/abs/1506.05869) * [Colornet - Neural Network to colorize grayscale images] (https://github.com/pavelgonchar/colornet) - Neural Network to colorize grayscale images * [Neural Caption Generator](https://github.com/jazzsaxmafia/show_attend_and_tell.tensorflow) - Implementation of ["Show and Tell"](http://arxiv.org/abs/1411.4555) * [Neural Caption Generator with Attention](https://github.com/jazzsaxmafia/show_attend_and_tell.tensorflow) - Implementation of ["Show, Attend and Tell"](http://arxiv.org/abs/1502.03044) * [Weakly_detector](https://github.com/jazzsaxmafia/Weakly_detector) - Implementation of ["Learning Deep Features for Discriminative Localization"](http://cnnlocalization.csail.mit.edu/) * [Dynamic Capacity Networks](https://github.com/jazzsaxmafia/dcn.tf) - Implementation of ["Dynamic Capacity Networks"](http://arxiv.org/abs/1511.07838) * [HMM in TensorFlow](https://github.com/dwiel/tensorflow_hmm) - Implementation of viterbi and forward/backward algorithms for HMM * [DeepOSM](https://github.com/trailbehind/DeepOSM) - Train TensorFlow neural nets with OpenStreetMap features and satellite imagery. * [DQN-tensorflow](https://github.com/devsisters/DQN-tensorflow) - Tensorflow implementation of DeepMind's 'Human-Level Control through Deep Reinforcement Learning' with OpenAI Gym by Devsisters.com * [Highway Network](https://github.com/fomorians/highway-cnn) - Tensorflow implementation of ["Training Very Deep Networks"](http://arxiv.org/abs/1507.06228) with a [blog post](https://medium.com/jim-fleming/highway-networks-with-tensorflow-1e6dfa667daa#.ndicn1i27) * [Sentence Classification with CNN](https://github.com/dennybritz/cnn-text-classification-tf) - Tensorflow implementation of ["Convolutional Neural Networks for Sentence Classification"](http://arxiv.org/abs/1408.5882) with a [blog post](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/) * [End-To-End Memory Networks](https://github.com/domluna/memn2n) - Implementation of [End-To-End Memory Networks](http://arxiv.org/abs/1503.08895) * [Character-Aware Neural Language Models](https://github.com/carpedm20/lstm-char-cnn-tensorflow) - Tensorflow implementation of [Character-Aware Neural Language Models](http://arxiv.org/abs/1508.06615) * [YOLO Tensorflow ++](https://github.com/thtrieu/yolotf) - Tensorflow implementation of 'YOLO: Real-Time Object Detection', with training and an actual support for real-time running on mobile devices. * [Wavenet](https://github.com/ibab/tensorflow-wavenet) - This is a TensorFlow implementation of the [WaveNet generative neural network architecture](https://deepmind.com/blog/wavenet-generative-model-raw-audio/) for audio generation. ## Powered by TensorFlow * [YOLO TensorFlow](https://github.com/gliese581gg/YOLO_tensorflow) - Implementation of 'YOLO : Real-Time Object Detection' * [Magenta](https://github.com/tensorflow/magenta) - Research project to advance the state of the art in machine intelligence for music and art generation ## Libraries * [Scikit Flow (TF Learn)](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/learn/python/learn) - Simplified interface for Deep/Machine Learning (now part of TensorFlow) * [tensorflow.rb](https://github.com/somaticio/tensorflow.rb) - TensorFlow native interface for ruby using SWIG- tflearn - Deep learning library featuring a higher-level API
- TensorFlow-Slim - High-level library for defining models
- TensorFrames - TensorFlow binding for Apache Spark
- caffe-tensorflow - Convert Caffe models to TensorFlow format
- keras - Minimal, modular deep learning library for TensorFlow and Theano
- SyntaxNet: Neural Models of Syntax - A TensorFlow implementation of the models described in Globally Normalized Transition-Based Neural Networks, Andor et al. (2016)
- TensorFlow: smarter machine learning, for everyone - An introduction to TensorFlow
- Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source - Release of SyntaxNet, "an open-source neural network framework implemented in TensorFlow that provides a foundation for Natural Language Understanding systems.
- Why TensorFlow will change the Game for AI
- TensorFlow for Poets - Goes over the implementation of TensorFlow
- Introduction to Scikit Flow - Simplified Interface to TensorFlow - Key Features Illustrated
- Building Machine Learning Estimator in TensorFlow - Understanding the Internals of TensorFlow Learn Estimators
- TensorFlow - Not Just For Deep Learning
- The indico Machine Learning Team's take on TensorFlow
- The Good, Bad, & Ugly of TensorFlow - A survey of six months rapid evolution (+ tips/hacks and code to fix the ugly stuff), Dan Kuster at Indico, May 9, 2016
- Fizz Buzz in TensorFlow - A joke by Joel Grus
If you want to contribute to this list (please do), send me a pull request or contact me @jtoy Also, if you notice that any of the above listed repositories should be deprecated, due to any of the following reasons:
- Repository's owner explicitly say that "this library is not maintained".
- Not committed for long time (2~3 years).
More info on the guidelines
## Credits