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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 | - [Tools/Utilities](#tools-utils) 21 | - [Videos](#video) 22 | - [Papers](#papers) 23 | - [Blog posts](#blogs) 24 | - [Community](#community) 25 | - [Books](#books) 26 | 27 | 28 | 29 | 30 | 31 | 32 | ## Tutorials 33 | 34 | * [TensorFlow Tutorial 1](https://github.com/pkmital/tensorflow_tutorials) - From the basics to slightly more interesting applications of TensorFlow 35 | * [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 36 | * [TensorFlow Tutorial 3](https://github.com/Hvass-Labs/TensorFlow-Tutorials) - These tutorials are intended for beginners in Deep Learning and TensorFlow with well-documented code and YouTube videos. 37 | * [TensorFlow Examples](https://github.com/aymericdamien/TensorFlow-Examples) - TensorFlow tutorials and code examples for beginners 38 | * [Sungjoon's TensorFlow-101](https://github.com/sjchoi86/Tensorflow-101) - TensorFlow tutorials written in Python with Jupyter Notebook 39 | * [Terry Um’s TensorFlow Exercises](https://github.com/terryum/TensorFlow_Exercises) - Re-create the codes from other TensorFlow examples 40 | * [Installing TensorFlow on Raspberry Pi 3](https://github.com/samjabrahams/tensorflow-on-raspberry-pi) - TensorFlow compiled and running properly on the Raspberry Pi 41 | * [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 42 | * [Getting Started with TensorFlow on Android](https://omid.al/posts/2017-02-20-Tutorial-Build-Your-First-Tensorflow-Android-App.html) - Build your first TensorFlow Android app 43 | * [Predict time series](https://github.com/guillaume-chevalier/seq2seq-signal-prediction) - Learn to use a seq2seq model on simple datasets as an introduction to the vast array of possibilities that this architecture offers 44 | * [Single Image Random Dot Stereograms](https://github.com/Mazecreator/TensorFlow-SIRDS) - SIRDS is a means to present 3D data in a 2D image. It allows for scientific data display of a waterfall type plot with no hidden lines due to perspective. 45 | * [CS20 SI: TensorFlow for DeepLearning Research](http://web.stanford.edu/class/cs20si/syllabus.html) - Stanford Course about Tensorflow from 2017 - [Syllabus](http://web.stanford.edu/class/cs20si/syllabus.html) - [Unofficial Videos](https://youtu.be/g-EvyKpZjmQ?list=PLSPPwKHXGS2110rEaNH7amFGmaD5hsObs) 46 | * [TensorFlow World](https://github.com/astorfi/TensorFlow-World) - Concise and ready-to-use TensorFlow tutorials with detailed documentation are provided. 47 | * [Effective Tensorflow](https://github.com/vahidk/EffectiveTensorflow) - TensorFlow howtos and best practices. Covers the basics as well as advanced topics. 48 | * [TensorLayer](http://tensorlayer.readthedocs.io/en/latest/user/tutorial.html) - Modular implementation for TensorFlow's official tutorials. ([CN](https://tensorlayercn.readthedocs.io/zh/latest/user/tutorial.html)). 49 | * [Understanding The Tensorflow Estimator API](https://www.lighttag.io/blog/tensorflow-estimator-api/) A conceptual overview of the Estimator API, when you'd use it and why. 50 | * [Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning](https://www.coursera.org/learn/introduction-tensorflow) - Introduction to Tensorflow offered by Coursera 51 | * [Convolutional Neural Networks in TensorFlow](https://www.coursera.org/learn/convolutional-neural-networks-tensorflow) - Convolutional Neural Networks in Tensorflow, offered by Coursera 52 | * [TensorLayerX](https://tensorlayerx.readthedocs.io/en/latest/index.html#user-guide) - Using TensorFlow like PyTorch. ([Api docs](https://tensorlayerx.readthedocs.io/en/latest/index.html#)) 53 | 54 | 55 | 56 | ## Models/Projects 57 | 58 | * [Tensorflow-Project-Template](https://github.com/Mrgemy95/Tensorflow-Project-Template) - A simple and well-designed template for your tensorflow project. 59 | * [Domain Transfer Network](https://github.com/yunjey/dtn-tensorflow) - Implementation of Unsupervised Cross-Domain Image Generation 60 | * [Show, Attend and Tell](https://github.com/yunjey/show_attend_and_tell) - Attention Based Image Caption Generator 61 | * [Neural Style](https://github.com/cysmith/neural-style-tf) Implementation of Neural Style 62 | * [SRGAN](https://github.com/tensorlayer/srgan) - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network 63 | * [Pretty Tensor](https://github.com/google/prettytensor) - Pretty Tensor provides a high level builder API 64 | * [Neural Style](https://github.com/anishathalye/neural-style) - An implementation of neural style 65 | * [AlexNet3D](https://github.com/denti/AlexNet3D) - An implementations of AlexNet3D. Simple AlexNet model but with 3D convolutional layers (conv3d). 66 | * [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 67 | * [NeuralArt](https://github.com/ckmarkoh/neuralart_tensorflow) - Implementation of A Neural Algorithm of Artistic Style 68 | * [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 69 | * [Neural Turing Machine in TensorFlow](https://github.com/carpedm20/NTM-tensorflow) - implementation of Neural Turing Machine 70 | * [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 71 | * [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 vice-versa. 72 | * [Chatbot](https://github.com/Conchylicultor/DeepQA) - Implementation of ["A neural conversational model"](http://arxiv.org/abs/1506.05869) 73 | * [Seq2seq-Chatbot](https://github.com/tensorlayer/seq2seq-chatbot) - Chatbot in 200 lines of code 74 | * [DCGAN](https://github.com/tensorlayer/dcgan) - Deep Convolutional Generative Adversarial Networks 75 | * [GAN-CLS](https://github.com/zsdonghao/text-to-image) -Generative Adversarial Text to Image Synthesis 76 | * [im2im](https://github.com/zsdonghao/Unsup-Im2Im) - Unsupervised Image to Image Translation with Generative Adversarial Networks 77 | * [Improved CycleGAN](https://github.com/luoxier/CycleGAN_Tensorlayer) - Unpaired Image to Image Translation 78 | * [DAGAN](https://github.com/nebulaV/DAGAN) - Fast Compressed Sensing MRI Reconstruction 79 | * [Colornet - Neural Network to colorize grayscale images](https://github.com/pavelgonchar/colornet) - Neural Network to colorize grayscale images 80 | * [Neural Caption Generator](https://github.com/jazzsaxmafia/show_attend_and_tell.tensorflow) - Implementation of ["Show and Tell"](http://arxiv.org/abs/1411.4555) 81 | * [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) 82 | * [Weakly_detector](https://github.com/jazzsaxmafia/Weakly_detector) - Implementation of ["Learning Deep Features for Discriminative Localization"](http://cnnlocalization.csail.mit.edu/) 83 | * [Dynamic Capacity Networks](https://github.com/jazzsaxmafia/dcn.tf) - Implementation of ["Dynamic Capacity Networks"](http://arxiv.org/abs/1511.07838) 84 | * [HMM in TensorFlow](https://github.com/dwiel/tensorflow_hmm) - Implementation of viterbi and forward/backward algorithms for HMM 85 | * [DeepOSM](https://github.com/trailbehind/DeepOSM) - Train TensorFlow neural nets with OpenStreetMap features and satellite imagery. 86 | * [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 87 | * [Policy Gradient](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_atari_pong.py) - For Playing Atari Ping Pong 88 | * [Deep Q-Network](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_frozenlake_dqn.py) - For Playing Frozen Lake Game 89 | * [AC](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_cartpole_ac.py) - Actor Critic for Playing Discrete Action space Game (Cartpole) 90 | * [A3C](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_bipedalwalker_a3c_continuous_action.py) - Asynchronous Advantage Actor Critic (A3C) for Continuous Action Space (Bipedal Walker) 91 | * [DAGGER](https://github.com/zsdonghao/Imitation-Learning-Dagger-Torcs) - For Playing [Gym Torcs](https://github.com/ugo-nama-kun/gym_torcs) 92 | * [TRPO](https://github.com/jjkke88/RL_toolbox) - For Continuous and Discrete Action Space by 93 | * [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) 94 | * [Hierarchical Attention Networks](https://github.com/tqtg/hierarchical-attention-networks) - TensorFlow implementation of ["Hierarchical Attention Networks for Document Classification"](https://www.cs.cmu.edu/~hovy/papers/16HLT-hierarchical-attention-networks.pdf) 95 | * [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/) 96 | * [End-To-End Memory Networks](https://github.com/domluna/memn2n) - Implementation of [End-To-End Memory Networks](http://arxiv.org/abs/1503.08895) 97 | * [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) 98 | * [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. 99 | * [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. 100 | * [Mnemonic Descent Method](https://github.com/trigeorgis/mdm) - Tensorflow implementation of ["Mnemonic Descent Method: A recurrent process applied for end-to-end face alignment"](http://ibug.doc.ic.ac.uk/media/uploads/documents/trigeorgis2016mnemonic.pdf) 101 | * [CNN visualization using Tensorflow](https://github.com/InFoCusp/tf_cnnvis) - Tensorflow implementation of ["Visualizing and Understanding Convolutional Networks"](https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf) 102 | * [VGAN Tensorflow](https://github.com/Singularity42/VGAN-Tensorflow) - Tensorflow implementation for MIT ["Generating Videos with Scene Dynamics"](http://carlvondrick.com/tinyvideo/) by Vondrick et al. 103 | * [3D Convolutional Neural Networks in TensorFlow](https://github.com/astorfi/3D-convolutional-speaker-recognition) - Implementation of ["3D Convolutional Neural Networks for Speaker Verification application"](https://arxiv.org/abs/1705.09422) in TensorFlow by Torfi et al. 104 | * [U-Net](https://github.com/zsdonghao/u-net-brain-tumor) - For Brain Tumor Segmentation 105 | * [Spatial Transformer Networks](https://github.com/zsdonghao/Spatial-Transformer-Nets) - Learn the Transformation Function 106 | * [Lip Reading - Cross Audio-Visual Recognition using 3D Architectures in TensorFlow](https://github.com/astorfi/lip-reading-deeplearning) - TensorFlow Implementation of ["Cross Audio-Visual Recognition in the Wild Using Deep Learning"](https://arxiv.org/abs/1706.05739) by Torfi et al. 107 | * [Attentive Object Tracking](https://github.com/akosiorek/hart) - Implementation of ["Hierarchical Attentive Recurrent Tracking"](https://arxiv.org/abs/1706.09262) 108 | * [Holographic Embeddings for Graph Completion and Link Prediction](https://github.com/laxatives/TensorFlow-TransX) - Implementation of [Holographic Embeddings of Knowledge Graphs](http://arxiv.org/abs/1510.04935) 109 | * [Unsupervised Object Counting](https://github.com/akosiorek/attend_infer_repeat) - Implementation of ["Attend, Infer, Repeat"](https://papers.nips.cc/paper/6230-attend-infer-repeat-fast-scene-understanding-with-generative-models) 110 | * [Tensorflow FastText](https://github.com/apcode/tensorflow_fasttext) - A simple embedding based text classifier inspired by Facebook's fastText. 111 | * [MusicGenreClassification](https://github.com/mlachmish/MusicGenreClassification) - Classify music genre from a 10 second sound stream using a Neural Network. 112 | * [Kubeflow](https://github.com/kubeflow/kubeflow) - Framework for easily using Tensorflow with Kubernetes. 113 | * [TensorNets](https://github.com/taehoonlee/tensornets) - 40+ Popular Computer Vision Models With Pre-trained Weights. 114 | * [Ladder Network](https://github.com/divamgupta/ladder_network_keras) - Implementation of Ladder Network for Semi-Supervised Learning in Keras and Tensorflow 115 | * [TF-Unet](https://github.com/juniorxsound/TF-Unet) - General purpose U-Network implemented in Keras for image segmentation 116 | * [Sarus TF2 Models](https://github.com/sarus-tech/tf2-published-models) - A long list of recent generative models implemented in clean, easy to reuse, Tensorflow 2 code (Plain Autoencoder, VAE, VQ-VAE, PixelCNN, Gated PixelCNN, PixelCNN++, PixelSNAIL, Conditional Neural Processes). 117 | * [Model Maker](https://www.tensorflow.org/lite/guide/model_maker) - A transfer learning library that simplifies the process of training, evaluation and deployment for TensorFlow Lite models (support: Image Classification, Object Detection, Text Classification, BERT Question Answer, Audio Classification, Recommendation etc.; [API reference](https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker)). 118 | 119 | 120 | 121 | 122 | ## Powered by TensorFlow 123 | 124 | * [YOLO TensorFlow](https://github.com/gliese581gg/YOLO_tensorflow) - Implementation of 'YOLO : Real-Time Object Detection' 125 | * [android-yolo](https://github.com/natanielruiz/android-yolo) - Real-time object detection on Android using the YOLO network, powered by TensorFlow. 126 | * [Magenta](https://github.com/tensorflow/magenta) - Research project to advance the state of the art in machine intelligence for music and art generation 127 | 128 | 129 | 130 | 131 | ## Libraries 132 | 133 | * [TensorFlow Estimators](https://www.tensorflow.org/guide/estimators) - high-level TensorFlow API that greatly simplifies machine learning programming (originally [tensorflow/skflow](https://github.com/tensorflow/skflow)) 134 | * [R Interface to TensorFlow](https://tensorflow.rstudio.com/) - R interface to TensorFlow APIs, including Estimators, Keras, Datasets, etc. 135 | * [Lattice](https://github.com/tensorflow/lattice) - Implementation of Monotonic Calibrated Interpolated Look-Up Tables in TensorFlow 136 | * [tensorflow.rb](https://github.com/somaticio/tensorflow.rb) - TensorFlow native interface for ruby using SWIG 137 | * [tflearn](https://github.com/tflearn/tflearn) - Deep learning library featuring a higher-level API 138 | * [TensorLayer](https://github.com/tensorlayer/tensorlayer) - Deep learning and reinforcement learning library for researchers and engineers 139 | * [TensorFlow-Slim](https://github.com/tensorflow/models/tree/master/inception/inception/slim) - High-level library for defining models 140 | * [TensorFrames](https://github.com/tjhunter/tensorframes) - TensorFlow binding for Apache Spark 141 | * [TensorForce](https://github.com/reinforceio/tensorforce) - TensorForce: A TensorFlow library for applied reinforcement learning 142 | * [TensorFlowOnSpark](https://github.com/yahoo/TensorFlowOnSpark) - initiative from Yahoo! to enable distributed TensorFlow with Apache Spark. 143 | * [caffe-tensorflow](https://github.com/ethereon/caffe-tensorflow) - Convert Caffe models to TensorFlow format 144 | * [keras](http://keras.io) - Minimal, modular deep learning library for TensorFlow and Theano 145 | * [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) 146 | * [keras-js](https://github.com/transcranial/keras-js) - Run Keras models (tensorflow backend) in the browser, with GPU support 147 | * [NNFlow](https://github.com/welschma/NNFlow) - Simple framework allowing to read-in ROOT NTuples by converting them to a Numpy array and then use them in Google Tensorflow. 148 | * [Sonnet](https://github.com/deepmind/sonnet) - Sonnet is DeepMind's library built on top of TensorFlow for building complex neural networks. 149 | * [tensorpack](https://github.com/ppwwyyxx/tensorpack) - Neural Network Toolbox on TensorFlow focusing on training speed and on large datasets. 150 | * [tf-encrypted](https://github.com/mortendahl/tf-encrypted) - Layer on top of TensorFlow for doing machine learning on encrypted data 151 | * [pytorch2keras](https://github.com/nerox8664/pytorch2keras) - Convert PyTorch models to Keras (with TensorFlow backend) format 152 | * [gluon2keras](https://github.com/stjordanis/gluon2keras) - Convert Gluon models to Keras (with TensorFlow backend) format 153 | * [TensorIO](https://doc-ai.github.io/tensorio/) - Lightweight, cross-platform library for deploying TensorFlow Lite models to mobile devices. 154 | * [StellarGraph](https://github.com/stellargraph/stellargraph) - Machine Learning on Graphs, a Python library for machine learning on graph-structured (network-structured) data. 155 | * [DeepBay](https://github.com/ElPapi42/DeepBay) - High-Level Keras Complement for implement common architectures stacks, served as easy to use plug-n-play modules 156 | * [Tensorflow-Probability](https://www.tensorflow.org/probability) - Probabilistic programming built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware. 157 | * [TensorLayerX](https://github.com/tensorlayer/TensorLayerX) - TensorLayerX: A Unified Deep Learning Framework for All Hardwares, Backends and OS, including TensorFlow. 158 | * [Txeo](https://github.com/rdabra/txeo) - A modern C++ wrapper for TensorFlow. 159 | 160 | 161 | 162 | ## Tools/Utilities 163 | 164 | * [Speedster](https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/speedster) - Automatically apply SOTA optimization techniques to achieve the maximum inference speed-up on your hardware. 165 | * [Guild AI](https://guild.ai) - Task runner and package manager for TensorFlow 166 | * [ML Workspace](https://github.com/ml-tooling/ml-workspace) - All-in-one web IDE for machine learning and data science. Combines Tensorflow, Jupyter, VS Code, Tensorboard, and many other tools/libraries into one Docker image. 167 | * [create-tf-app](https://github.com/radi-cho/create-tf-app) - Project builder command line tool for Tensorflow covering environment management, linting, and logging. 168 | 169 | 170 | 171 | ## Videos 172 | 173 | * [TensorFlow Guide 1](http://bit.ly/1OX8s8Y) - A guide to installation and use 174 | * [TensorFlow Guide 2](http://bit.ly/1R27Ki9) - Continuation of first video 175 | * [TensorFlow Basic Usage](http://bit.ly/1TCNmEY) - A guide going over basic usage 176 | * [TensorFlow Deep MNIST for Experts](http://bit.ly/1L9IfJx) - Goes over Deep MNIST 177 | * [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 178 | * [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) 179 | * [Videos from TensorFlow Silicon Valley Meet Up 1/19/2016](http://blog.altoros.com/videos-from-tensorflow-silicon-valley-meetup-january-19-2016.html) 180 | * [Videos from TensorFlow Silicon Valley Meet Up 1/21/2016](http://blog.altoros.com/videos-from-tensorflow-seattle-meetup-jan-21-2016.html) 181 | * [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 182 | * [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 183 | * [Large Scale Deep Learning with TensorFlow](https://youtu.be/XYwIDn00PAo) - Spark Summit 2016 Keynote by Jeff Dean 184 | * [Tensorflow and deep learning - without at PhD](https://www.youtube.com/watch?v=vq2nnJ4g6N0) - by Martin Görner 185 | * [Tensorflow and deep learning - without at PhD, Part 2 (Google Cloud Next '17)](https://www.youtube.com/watch?v=fTUwdXUFfI8) - by Martin Görner 186 | * [Image recognition in Go using TensorFlow](https://youtu.be/P8MZ1Z2LHrw) - by Alex Pliutau 187 | 188 | 189 | 190 | 191 | 192 | ## Papers 193 | 194 | * [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 195 | * [TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks](https://arxiv.org/pdf/1708.02637.pdf) 196 | * [TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning](https://arxiv.org/abs/1612.04251) 197 | * [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 198 | * [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) 199 | * [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). 200 | * [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 201 | * [TensorLayer: A Versatile Library for Efficient Deep Learning Development](https://arxiv.org/abs/1707.08551) - This paper describes a versatile Python library that aims at helping researchers and engineers efficiently develop deep learning systems. (Winner of The Best Open Source Software Award of ACM MM 2017) 202 | 203 | 204 | 205 | ## Official announcements 206 | 207 | * [TensorFlow: smarter machine learning, for everyone](https://googleblog.blogspot.com/2015/11/tensorflow-smarter-machine-learning-for.html) - An introduction to TensorFlow 208 | * [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. 209 | 210 | ## Blog posts 211 | * [Official Tensorflow Blog](http://blog.tensorflow.org/) 212 | * [Why TensorFlow will change the Game for AI](https://archive.fo/o9asj) 213 | * [TensorFlow for Poets](http://petewarden.com/2016/02/28/tensorflow-for-poets) - Goes over the implementation of TensorFlow 214 | * [Introduction to Scikit Flow - Simplified Interface to TensorFlow](http://terrytangyuan.github.io/2016/03/14/scikit-flow-intro/) - Key Features Illustrated 215 | * [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 216 | * [TensorFlow - Not Just For Deep Learning](http://terrytangyuan.github.io/2016/08/06/tensorflow-not-just-deep-learning/) 217 | * [The indico Machine Learning Team's take on TensorFlow](https://indico.io/blog/indico-tensorflow) 218 | * [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 219 | * [Fizz Buzz in TensorFlow](http://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/) - A joke by Joel Grus 220 | * [RNNs In TensorFlow, A Practical Guide And Undocumented Features](http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/) - Step-by-step guide with full code examples on GitHub. 221 | * [Using TensorBoard to Visualize Image Classification Retraining in TensorFlow](http://maxmelnick.com/2016/07/04/visualizing-tensorflow-retrain.html) 222 | * [TFRecords Guide](http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/) semantic segmentation and handling the TFRecord file format. 223 | * [TensorFlow Android Guide](https://blog.mindorks.com/android-tensorflow-machine-learning-example-ff0e9b2654cc) - Android TensorFlow Machine Learning Example. 224 | * [TensorFlow Optimizations on Modern Intel® Architecture](https://software.intel.com/en-us/articles/tensorflow-optimizations-on-modern-intel-architecture) - Introduces TensorFlow optimizations on Intel® Xeon® and Intel® Xeon Phi™ processor-based platforms based on an Intel/Google collaboration. 225 | * [Coca-Cola's Image Recognition App](https://developers.googleblog.com/2017/09/how-machine-learning-with-tensorflow.html) Coca-Cola's product code image recognizing neural network with user input feedback loop. 226 | * [How Does The TensorFlow Work](https://www.letslearnai.com/2018/02/02/how-does-the-machine-learning-library-tensorflow-work.html) How Does The Machine Learning Library TensorFlow Work? 227 | 228 | 229 | 230 | 231 | ## Community 232 | 233 | * [Stack Overflow](http://stackoverflow.com/questions/tagged/tensorflow) 234 | * [@TensorFlow on Twitter](https://twitter.com/tensorflow) 235 | * [Reddit](https://www.reddit.com/r/tensorflow) 236 | * [Mailing List](https://groups.google.com/a/tensorflow.org/forum/#!forum/discuss) 237 | 238 | 239 | 240 | 241 | ## Books 242 | 243 | * [Machine Learning with TensorFlow](http://tensorflowbook.com) by Nishant Shukla, computer vision researcher at UCLA and author of Haskell Data Analysis Cookbook. This book makes the math-heavy topic of ML approachable and practicle to a newcomer. 244 | * [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 245 | * [Deep Learning with Python](https://machinelearningmastery.com/deep-learning-with-python/) - Develop Deep Learning Models on Theano and TensorFlow Using Keras by Jason Brownlee 246 | * [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 environments - Bleeding Edge Press 247 | * [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 248 | * [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). 249 | * [Building Machine Learning Projects with Tensorflow](https://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-projects-tensorflow) – by Rodolfo Bonnin. This book covers various projects in TensorFlow that expose what can be done with TensorFlow in different scenarios. The book provides projects on training models, machine learning, deep learning, and working with various neural networks. Each project is an engaging and insightful exercise that will teach you how to use TensorFlow and show you how layers of data can be explored by working with Tensors. 250 | * [Deep Learning using TensorLayer](http://www.broadview.com.cn/book/5059) - by Hao Dong et al. This book covers both deep learning and the implementation by using TensorFlow and TensorLayer. 251 | * [TensorFlow 2.0 in Action](https://www.manning.com/books/tensorflow-in-action) - by Thushan Ganegedara. This practical guide to building deep learning models with the new features of TensorFlow 2.0 is filled with engaging projects, simple language, and coverage of the latest algorithms. 252 | * [Probabilistic Programming and Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - by Cameron Davidson-Pilon. Introduction to Bayesian methods and probabilistic graphical models using tensorflow-probability (and, alternatively PyMC2/3). 253 | 254 | 255 | 256 | 257 | 258 | ## Contributions 259 | 260 | Your contributions are always welcome! 261 | 262 | If you want to contribute to this list (please do), send me a pull request or contact me [@jtoy](https://twitter.com/jtoy) 263 | Also, if you notice that any of the above listed repositories should be deprecated, due to any of the following reasons: 264 | 265 | * Repository's owner explicitly say that "this library is not maintained". 266 | * Not committed for long time (2~3 years). 267 | 268 | More info on the [guidelines](https://github.com/jtoy/awesome-tensorflow/blob/master/contributing.md) 269 | 270 | 271 | 272 | 273 | ## Credits 274 | 275 | * Some of the python libraries were cut-and-pasted from [vinta](https://github.com/vinta/awesome-python) 276 | * The few go reference I found where pulled from [this page](https://code.google.com/p/go-wiki/wiki/Projects#Machine_Learning) 277 | 278 | -------------------------------------------------------------------------------- /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 | --------------------------------------------------------------------------------