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 ## Models/Projects * [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. * [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/) ## 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) * [tflearn](https://github.com/tflearn/tflearn) - Deep learning library featuring a higher-level API * [TensorFlow-Slim](https://github.com/tensorflow/models/tree/master/inception/inception/slim) - High-level library for defining models * [TensorFrames](https://github.com/tjhunter/tensorframes) - TensorFlow binding for Apache Spark * [caffe-tensorflow](https://github.com/ethereon/caffe-tensorflow) - Convert Caffe models to TensorFlow format * [keras](http://keras.io) - Minimal, modular deep learning library for TensorFlow and Theano * [SyntaxNet: Neural Models of Syntax](https://github.com/tensorflow/models/tree/master/syntaxnet) - A TensorFlow implementation of the models described in [Globally Normalized Transition-Based Neural Networks, Andor et al. (2016)](http://arxiv.org/pdf/1603.06042.pdf) ##Videos * [TensorFlow Guide 1](http://bit.ly/1OX8s8Y) - A guide to installation and use * [TensorFlow Guide 2](http://bit.ly/1R27Ki9) - Continuation of first video * [TensorFlow Basic Usage](http://bit.ly/1TCNmEY) - A guide going over basic usage * [TensorFlow Deep MNIST for Experts](http://bit.ly/1L9IfJx) - Goes over Deep MNIST * [TensorFlow Udacity Deep Learning](https://www.youtube.com/watch?v=ReaxoSIM5XQ) - Basic steps to install TensorFlow for free on the Cloud 9 online service with 1Gb of data * [Why Google wants everyone to have access to TensorFlow](http://video.foxnews.com/v/4611174773001/why-google-wants-everyone-to-have-access-to-tensorflow/?#sp=show-clips) * [Videos from TensorFlow Silicon Valley Meet Up 1/19/2016](http://blog.altoros.com/videos-from-tensorflow-silicon-valley-meetup-january-19-2016.html) * [Videos from TensorFlow Silicon Valley Meet Up 1/21/2016](http://blog.altoros.com/videos-from-tensorflow-seattle-meetup-jan-21-2016.html) * [Stanford CS224d Lecture 7 - Introduction to TensorFlow, 19th Apr 2016](https://www.youtube.com/watch?v=L8Y2_Cq2X5s&index=7&list=PLmImxx8Char9Ig0ZHSyTqGsdhb9weEGam) - CS224d Deep Learning for Natural Language Processing by Richard Socher * [Diving into Machine Learning through TensorFlow](https://youtu.be/GZBIPwdGtkk?list=PLBkISg6QfSX9HL6us70IBs9slFciFFa4W) - Pycon 2016 Portland Oregon, [Slide](https://storage.googleapis.com/amy-jo/talks/tf-workshop.pdf) & [Code](https://github.com/amygdala/tensorflow-workshop) by Julia Ferraioli, Amy Unruh, Eli Bixby * [Large Scale Deep Learning with TensorFlow](https://youtu.be/XYwIDn00PAo) - Spark Summit 2016 Keynote by Jeff Dean ##Papers * [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](http://download.tensorflow.org/paper/whitepaper2015.pdf) - This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google * [Comparative Study of Deep Learning Software Frameworks](http://arxiv.org/abs/1511.06435) - The study is performed on several types of deep learning architectures and we evaluate the performance of the above frameworks when employed on a single machine for both (multi-threaded) CPU and GPU (Nvidia Titan X) settings * [Distributed TensorFlow with MPI](http://arxiv.org/abs/1603.02339) - In this paper, we extend recently proposed Google TensorFlow for execution on large scale clusters using Message Passing Interface (MPI) * [Globally Normalized Transition-Based Neural Networks](http://arxiv.org/abs/1603.06042) - This paper describes the models behind [SyntaxNet](https://github.com/tensorflow/models/tree/master/syntaxnet). * [TensorFlow: A system for large-scale machine learning](https://arxiv.org/abs/1605.08695) - This paper describes the TensorFlow dataflow model in contrast to existing systems and demonstrate the compelling performance- 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
- 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, when you noticed that listed repository should be deprecated.
- 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