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In other words, the best way to build deep learning models. 8 | 9 | More info [here](http://tensorflow.org). 10 | 11 | 12 | 13 | ## Table of Contents 14 | 15 | 16 | - [Tutorials](#github-tutorials) 17 | - [Models/Projects](#github-projects) 18 | - [Powered by TensorFlow](#github-powered-by) 19 | - [Libraries](#libraries) 20 | - [Videos](#video) 21 | - [Papers](#papers) 22 | - [Blog posts](#blogs) 23 | - [Community](#community) 24 | - [Books](#books) 25 | 26 | 27 | 28 | 29 | 30 | ## Tutorials 31 | * [TensorFlow Tutorial 1](https://github.com/pkmital/tensorflow_tutorials) - From the basics to slightly more interesting applications of TensorFlow 32 | * [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 33 | * [TensorFlow Examples](https://github.com/aymericdamien/TensorFlow-Examples) - TensorFlow tutorials and code examples for beginners 34 | * [Sungjoon's TensorFlow-101](https://github.com/sjchoi86/Tensorflow-101) - TensorFlow tutorials written in Python with Jupyter Notebook 35 | * [Terry Um’s TensorFlow Exercises](https://github.com/terryum/TensorFlow_Exercises) - Re-create the codes from other TensorFlow examples 36 | * [Installing TensorFlow on Raspberry Pi 3](https://github.com/samjabrahams/tensorflow-on-raspberry-pi) - TensorFlow compiled and running properly on the Raspberry Pi 37 | * [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 38 | 39 | 40 | 41 | ## Models/Projects 42 | * [Show, Attend and Tell] (https://github.com/yunjey/show_attend_and_tell) - Attention Based Image Caption Generator 43 | * [Pretty Tensor](https://github.com/google/prettytensor) - Pretty Tensor provides a high level builder API 44 | * [Neural Style](https://github.com/anishathalye/neural-style) - An implementation of neural style 45 | * [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 46 | * [NeuralArt](https://github.com/ckmarkoh/neuralart_tensorflow) - Implementation of A Neural Algorithm of Artistic Style 47 | * [Deep-Q learning Pong with TensorFlow and PyGame](http://www.danielslater.net/2016/03/deep-q-learning-pong-with-tensorflow.html) 48 | * [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 49 | * [Neural Turing Machine in TensorFlow](https://github.com/carpedm20/NTM-tensorflow) - implementation of Neural Turing Machine 50 | * [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 51 | * [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. 52 | * [Chatbot](https://github.com/Conchylicultor/DeepQA) - Implementation of ["A neural conversational model"](http://arxiv.org/abs/1506.05869) 53 | * [Colornet - Neural Network to colorize grayscale images] (https://github.com/pavelgonchar/colornet) - Neural Network to colorize grayscale images 54 | * [Neural Caption Generator](https://github.com/jazzsaxmafia/show_attend_and_tell.tensorflow) - Implementation of ["Show and Tell"](http://arxiv.org/abs/1411.4555) 55 | * [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) 56 | * [Weakly_detector](https://github.com/jazzsaxmafia/Weakly_detector) - Implementation of ["Learning Deep Features for Discriminative Localization"](http://cnnlocalization.csail.mit.edu/) 57 | * [Dynamic Capacity Networks](https://github.com/jazzsaxmafia/dcn.tf) - Implementation of ["Dynamic Capacity Networks"](http://arxiv.org/abs/1511.07838) 58 | * [HMM in TensorFlow](https://github.com/dwiel/tensorflow_hmm) - Implementation of viterbi and forward/backward algorithms for HMM 59 | * [DeepOSM](https://github.com/trailbehind/DeepOSM) - Train TensorFlow neural nets with OpenStreetMap features and satellite imagery. 60 | * [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 61 | * [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) 62 | * [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/) 63 | * [End-To-End Memory Networks](https://github.com/domluna/memn2n) - Implementation of [End-To-End Memory Networks](http://arxiv.org/abs/1503.08895) 64 | * [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) 65 | * [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. 66 | 67 | 68 | ## Powered by TensorFlow 69 | * [YOLO TensorFlow](https://github.com/gliese581gg/YOLO_tensorflow) - Implementation of 'YOLO : Real-Time Object Detection' 70 | * [Magenta](https://github.com/tensorflow/magenta) - Research project to advance the state of the art in machine intelligence for music and art generation 71 | 72 | 73 | 74 | ## Libraries 75 | * [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) 76 | * [tensorflow.rb](https://github.com/somaticio/tensorflow.rb) - TensorFlow native interface for ruby using SWIG 77 | 78 | * [tflearn](https://github.com/tflearn/tflearn) - Deep learning library featuring a higher-level API 79 | * [TensorFlow-Slim](https://github.com/tensorflow/models/tree/master/inception/inception/slim) - High-level library for defining models 80 | * [TensorFrames](https://github.com/tjhunter/tensorframes) - TensorFlow binding for Apache Spark 81 | * [caffe-tensorflow](https://github.com/ethereon/caffe-tensorflow) - Convert Caffe models to TensorFlow format 82 | * [keras](http://keras.io) - Minimal, modular deep learning library for TensorFlow and Theano 83 | * [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) 84 | 85 | 86 | ##Videos 87 | * [TensorFlow Guide 1](http://bit.ly/1OX8s8Y) - A guide to installation and use 88 | * [TensorFlow Guide 2](http://bit.ly/1R27Ki9) - Continuation of first video 89 | * [TensorFlow Basic Usage](http://bit.ly/1TCNmEY) - A guide going over basic usage 90 | * [TensorFlow Deep MNIST for Experts](http://bit.ly/1L9IfJx) - Goes over Deep MNIST 91 | * [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 92 | * [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) 93 | * [Videos from TensorFlow Silicon Valley Meet Up 1/19/2016](http://blog.altoros.com/videos-from-tensorflow-silicon-valley-meetup-january-19-2016.html) 94 | * [Videos from TensorFlow Silicon Valley Meet Up 1/21/2016](http://blog.altoros.com/videos-from-tensorflow-seattle-meetup-jan-21-2016.html) 95 | * [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 96 | * [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 97 | * [Large Scale Deep Learning with TensorFlow](https://youtu.be/XYwIDn00PAo) - Spark Summit 2016 Keynote by Jeff Dean 98 | 99 | 100 | 101 | 102 | ##Papers 103 | * [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 104 | * [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 105 | * [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) 106 | * [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). 107 | * [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 108 | 109 | 110 | 111 | ## Official announcements 112 | 113 | * [TensorFlow: smarter machine learning, for everyone](https://googleblog.blogspot.com/2015/11/tensorflow-smarter-machine-learning-for.html) - An introduction to TensorFlow 114 | * [Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source](http://googleresearch.blogspot.com/2016/05/announcing-syntaxnet-worlds-most.html) - Release of SyntaxNet, "an open-source neural network framework implemented in TensorFlow that provides a foundation for Natural Language Understanding systems. 115 | 116 | ## Blog posts 117 | * [Why TensorFlow will change the Game for AI](http://www.somatic.io/blog/why-tensorflow-will-change-the-game-for-ai) 118 | * [TensorFlow for Poets](http://petewarden.com/2016/02/28/tensorflow-for-poets) - Goes over the implementation of TensorFlow 119 | * [Introduction to Scikit Flow - Simplified Interface to TensorFlow](http://terrytangyuan.github.io/2016/03/14/scikit-flow-intro/) - Key Features Illustrated 120 | * [Building Machine Learning Estimator in TensorFlow](http://terrytangyuan.github.io/2016/07/08/understand-and-build-tensorflow-estimator/) - Understanding the Internals of TensorFlow Learn Estimators 121 | * [TensorFlow - Not Just For Deep Learning](http://terrytangyuan.github.io/2016/08/06/tensorflow-not-just-deep-learning/) 122 | * [The indico Machine Learning Team's take on TensorFlow](https://indico.io/blog/indico-tensorflow) 123 | * [The Good, Bad, & Ugly of TensorFlow](https://indico.io/blog/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 124 | * [Fizz Buzz in TensorFlow](http://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/) - A joke by Joel Grus 125 | 126 | 127 | 128 | ## Community 129 | * [Stack Overflow](http://stackoverflow.com/questions/tagged/tensorflow) 130 | * [@TensorFlo on Twitter](https://twitter.com/TensorFlo) 131 | * [Reddit](https://www.reddit.com/r/tensorflow) 132 | * [Mailing List](https://groups.google.com/a/tensorflow.org/forum/#!forum/discuss) 133 | 134 | 135 | 136 | ## Books 137 | * [First Contact with TensorFlow](http://www.jorditorres.org/first-contact-with-tensorflow/) by Jordi Torres, professor at UPC Barcelona Tech and a research manager and senior advisor at Barcelona Supercomputing Center 138 | * [Deep Learning with Python](https://machinelearningmastery.com/deep-learning-with-python/) - Develop Deep Learning Models on Theano and TensorFlow Using Keras by Jason Brownlee 139 | * [Tensorflow for Machine Intelligence](https://bleedingedgepress.com/tensor-flow-for-machine-intelligence/) - Complete guide to use Tensorflow from the basics of graph computing, to deep learning models to using it in production environmemts - Bleeding Edge Press 140 | * [Getting Started with TensorFlow](https://www.packtpub.com/big-data-and-business-intelligence/getting-started-tensorflow) - Get up and running with the latest numerical computing library by Google and dive deeper into your data, by Giancarlo Zaccone 141 | * [Hands-On Machine Learning with Scikit-Learn and TensorFlow](http://shop.oreilly.com/product/0636920052289.do) – by Aurélien Geron, former lead of the YouTube video classification team. Covers ML fundamentals, training and deploying deep nets across multiple servers and GPUs using TensorFlow, the latest CNN, RNN and Autoencoder architectures, and Reinforcement Learning (Deep Q). 142 | 143 | 144 | 145 | ## Contributions 146 | Your contributions are always welcome! 147 | 148 | If you want to contribute to this list (please do), send me a pull request or contact me [@jtoy](https://twitter.com/jtoy) 149 | Also, when you noticed that listed repository should be deprecated. 150 | 151 | * Repository's owner explicitly say that "this library is not maintained". 152 | * Not committed for long time (2~3 years). 153 | 154 | More info on the [guidelines](https://github.com/jtoy/awesome-tensorflow/blob/master/contributing.md) 155 | 156 | 157 | 158 | ## Credits 159 | 160 | * Some of the python libraries were cut-and-pasted from [vinta](https://github.com/vinta/awesome-python) 161 | * The few go reference I found where pulled from [this page](https://code.google.com/p/go-wiki/wiki/Projects#Machine_Learning) 162 | -------------------------------------------------------------------------------- /contributing.md: -------------------------------------------------------------------------------- 1 | # Contribution Guidelines 2 | 3 | Please note that this project is released with a [Contributor Code of Conduct](code-of-conduct.md). By participating in this project you agree to abide by its terms. 4 | 5 | ## Table of Contents 6 | 7 | - [Adding to this list](#adding-to-this-list) 8 | - [Creating your own awesome list](#creating-your-own-awesome-list) 9 | - [Adding something to an awesome list](#adding-something-to-an-awesome-list) 10 | - [Updating your Pull Request](#updating-your-pull-request) 11 | 12 | ## Adding to this list 13 | 14 | Please ensure your pull request adheres to the following guidelines: 15 | 16 | - Search previous suggestions before making a new one, as yours may be a duplicate. 17 | - Make sure the list is useful before submitting. That implies it has enough content and every item has a good succinct description. 18 | - Make an individual pull request for each suggestion. 19 | - Use [title-casing](http://titlecapitalization.com) (AP style). 20 | - Use the following format: `[List Name](link)` 21 | - Link additions should be added to the bottom of the relevant category. 22 | - New categories or improvements to the existing categorization are welcome. 23 | - Check your spelling and grammar. 24 | - Make sure your text editor is set to remove trailing whitespace. 25 | - The pull request and commit should have a useful title. 26 | - The body of your commit message should contain a link to the repository. 27 | 28 | Thank you for your suggestions! 29 | 30 | ## Creating your own awesome list 31 | 32 | To create your own list, check out the [instructions](create-list.md). 33 | 34 | ## Adding something to an awesome list 35 | 36 | If you have something awesome to contribute to an awesome list, this is how you do it. 37 | 38 | You'll need a [GitHub account](https://github.com/join)! 39 | 40 | 1. Access the awesome list's GitHub page. For example: https://github.com/sindresorhus/awesome 41 | 2. Click on the `readme.md` file: ![Step 2 Click on Readme.md](https://cloud.githubusercontent.com/assets/170270/9402920/53a7e3ea-480c-11e5-9d81-aecf64be55eb.png) 42 | 3. Now click on the edit icon. ![Step 3 - Click on Edit](https://cloud.githubusercontent.com/assets/170270/9402927/6506af22-480c-11e5-8c18-7ea823530099.png) 43 | 4. You can start editing the text of the file in the in-browser editor. Make sure you follow guidelines above. You can use [GitHub Flavored Markdown](https://help.github.com/articles/github-flavored-markdown/). ![Step 4 - Edit the file](https://cloud.githubusercontent.com/assets/170270/9402932/7301c3a0-480c-11e5-81f5-7e343b71674f.png) 44 | 5. Say why you're proposing the changes, and then click on "Propose file change". ![Step 5 - Propose Changes](https://cloud.githubusercontent.com/assets/170270/9402937/7dd0652a-480c-11e5-9138-bd14244593d5.png) 45 | 6. Submit the [pull request](https://help.github.com/articles/using-pull-requests/)! 46 | 47 | ## Updating your Pull Request 48 | 49 | Sometimes, a maintainer of an awesome list will ask you to edit your Pull Request before it is included. This is normally due to spelling errors or because your PR didn't match the awesome-* list guidelines. 50 | 51 | [Here](https://github.com/RichardLitt/docs/blob/master/amending-a-commit-guide.md) is a write up on how to change a Pull Request, and the different ways you can do that. 52 | --------------------------------------------------------------------------------