TensorFlow 中文资源精选, 官方网站, 安装教程, 入门教程, 视频教程, 实战项目, 学习路径. https://github.com/fendouai/Awesome-TensorFlow-Chinese
官方网站, 初步了解.
安装教程, 安装之后跑起来.
入门教程, 简单的模型学习和运行.
实战项目, 根据自己的需求进行开发.
很多内容下面这个英文项目:
- Inspired by
- https://github.com/jtoy/awesome-tensorflow
官方网站
官网: https://www.tensorflow.org/
中文: https://tensorflow.google.cn/
GitHub:https://github.com/tensorflow
安装教程
中文安装教程
Mac 安装: http://www.cnblogs.com/tensorflownews/p/7298646.html
极客学院教程: http://wiki.jikexueyuan.com/project/tensorflow-zh/get_started/os_setup.html
官方安装教程 (建议用官方教程, 现在官网可以直接访问了.)
- Mac:https://tensorflow.google.cn/install/install_mac
- Windows:https://tensorflow.google.cn/install/install_windows
- Linux:https://tensorflow.google.cn/install/install_linux
入门教程
官方入门教程
开始学习: https://tensorflow.google.cn/get_started/
MNIST 针对初学者的字体识别: https://tensorflow.google.cn/get_started/mnist/beginners
MNIST 针对专业的深度字体识别: https://tensorflow.google.cn/get_started/mnist/pros
入门教程
极客学院: http://wiki.jikexueyuan.com/project/tensorflow-zh/
大 U 的技术课堂: https://zhuanlan.zhihu.com/p/22410917
TensorFlowNews:https://zhuanlan.zhihu.com/TensorFlownews
实战项目
官方实战项目
- Models built with TensorFlow
- models:https://github.com/tensorflow/models
- Magenta: Music and Art Generation with Machine Intelligence
- magenta:https://github.com/tensorflow/magenta
- TensorFlow Neural Machine Translation Tutorial
- nmt:https://github.com/tensorflow/nmt
书籍 (推荐)
- Deep Learning
- Deep Learning An MIT Press book
Deep Learning 中文翻译
Deep Learning Book Chinese Translation
社区群组
QQ 群
522785813
微信群
微信群二维码有效期太短了, 我博客保持更新.
http://www.tensorflownews.com/
我系统的学习了两个月之后做的几个项目.
TensorFlow 卷积神经网络 Model Project:
FaceRank - Rank Face by CNN Model based on TensorFlow (add keras version). FaceRank - 人脸打分基于 TensorFlow (新增 Keras 版本) 的 CNN 模型 (可能是最有趣的 TensorFlow 中文入门实战项目)
https://github.com/fendouai/FaceRank
TensorFlow 循环神经网络 Model Project:
一个比特币交易机器人基于 Tensorflow LSTM 模型, 仅供娱乐. A Bitcoin trade robot based on Tensorflow LSTM model.Just for fun.
- https://github.com/TensorFlowNews/TensorFlow-Bitcoin-Robot
- TensorFlow Seq2Seq Model Project:
ChatGirl is an AI ChatBot based on TensorFlow Seq2Seq Model.ChatGirl 一个基于 TensorFlow Seq2Seq 模型的聊天机器人.(包含预处理过的 twitter 英文数据集, 训练, 运行, 工具代码, 可以运行但是效果有待提高.)
https://github.com/fendouai/ChatGirl
教程
TensorFlow Examples https://github.com/aymericdamien/TensorFlow-Examples - 针对初学者的 TensorFlow 教程和代码
TensorFlow Tutorial https://github.com/pkmital/tensorflow_tutorials - 从基础知识到有趣的 tensorflow 应用
TensorFlow Tutorial https://github.com/nlintz/TensorFlow-Tutorials - 基于谷歌的 TensorFlow 框架介绍深度学习
Sungjoon's TensorFlow-101 https://github.com/sjchoi86/Tensorflow-101 - TensorFlow 教程用 Python 的 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
- 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
- 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
- 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.
- 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
- TensorFlow World https://github.com/astorfi/TensorFlow-World - Concise and ready-to-use TensorFlow tutorials with detailed documentation are provided.
- Effective Tensorflow https://github.com/vahidk/EffectiveTensorflow - Tensorflow howtos and best practices. Covers the basics as well as advanced topics.
模型项目
- Domain Transfer Network https: //github.com/yunjey/dtn-tensorflow - Implementation of Unsupervised Cross-Domain Image Generation
- Show,
- Attend and Tell https: //github.com/yunjey/show_attend_and_tell - Attention Based Image Caption Generator
- Neural Style https: //github.com/cysmith/neural-style-tf Implementation of Neural Style
- 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
- https: //github.com/denti/AlexNet3D - An implementations of AlexNet3D. Simple AlexNet model but with 3D convolutional layers (conv3d).
- 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
- 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
- https: //github.com/trailbehind/DeepOSM - Train TensorFlow neural nets with OpenStreetMap features and satellite imagery.
- 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.
- 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
- 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
- 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.
- 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.
- 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.
基于 TensorFlow 的产品
- YOLO TensorFlow https://github.com/gliese581gg/YOLO_tensorflow - Implementation of 'YOLO : Real-Time Object Detection'
- https://github.com/natanielruiz/android-yolo - Real-time object detection on Android using the YOLO network, powered by TensorFlow.
- Magenta https://github.com/tensorflow/magenta - Research project to advance the state of the art in machine intelligence for music and art generation
库
- tf.contrib.learn https: //github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/learn/python/learn - Simplified interface for Deep/Machine Learning (now part of TensorFlow)
- https: //github.com/somaticio/tensorflow.rb - TensorFlow native interface for ruby using SWIG
- 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
- https: //github.com/yahoo/TensorFlowOnSpark - initiative from Yahoo! to enable distributed TensorFlow with Apache Spark.
- https: //github.com/ethereon/caffe-tensorflow - Convert Caffe models to TensorFlow format
- 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
- https: //github.com/transcranial/keras-js - Run Keras models (tensorflow backend) in the browser, with GPU support
- 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.
- Sonnet https: //github.com/deepmind/sonnet - Sonnet is DeepMind's library built on top of TensorFlow for building complex neural networks.
- https: //github.com/ppwwyyxx/tensorpack - Neural Network Toolbox on TensorFlow focusing on training speed and on large datasets.
视频
- 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
- Tensorflow and deep learning - without at PhD https://www.youtube.com/watch?v=vq2nnJ4g6N0 - by Martin Görner
- Tensorflow and deep learning - without at PhD, Part 2 (Google Cloud Next '17) https://www.youtube.com/watch?v=fTUwdXUFfI8 - by Martin Görner
论文
- 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
- TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning https://arxiv.org/abs/1612.04251
- 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
官方博客
- TensorFlow: smarter machine learning, for everyone https://googleblog.blogspot.com/2015/11/tensorflow-smarter-machine-learning-for.html - An introduction to TensorFlow
- 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.
博客文章
- Why TensorFlow will change the Game for AI http://www.somatic.io/blog/why-tensorflow-will-change-the-game-for-ai
- TensorFlow for Poets http://petewarden.com/2016/02/28/tensorflow-for-poets - Goes over the implementation of TensorFlow
- Introduction to Scikit Flow - Simplified Interface to TensorFlow http://terrytangyuan.github.io/2016/03/14/scikit-flow-intro/ - Key Features Illustrated
- 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
- TensorFlow - Not Just For Deep Learning http://terrytangyuan.github.io/2016/08/06/tensorflow-not-just-deep-learning/
- The indico Machine Learning Team's take on TensorFlow https://indico.io/blog/indico-tensorflow
- 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
- Fizz Buzz in TensorFlow http://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/ - A joke by Joel Grus
- 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.
- Using TensorBoard to Visualize Image Classification Retraining in TensorFlow http://maxmelnick.com/2016/07/04/visualizing-tensorflow-retrain.html
- TFRecords Guide http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/ semantic segmentation and handling the TFRecord file format.
- TensorFlow Android Guide https://blog.mindorks.com/android-tensorflow-machine-learning-example-ff0e9b2654cc - Android TensorFlow Machine Learning Example.
- 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.
社区
- Stack Overflow http://stackoverflow.com/questions/tagged/tensorflow
- @TensorFlow on Twitter https://twitter.com/tensorflow
- Reddit https://www.reddit.com/r/tensorflow
- Mailing List https://groups.google.com/a/tensorflow.org/forum/#!forum/discuss
书籍
- 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.
- 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
- Deep Learning with Python https://machinelearningmastery.com/deep-learning-with-python/ - Develop Deep Learning Models on Theano and TensorFlow Using Keras by Jason Brownlee
- 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
- 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
- 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).
- 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.
来源: http://www.tensorflownews.com/2017/09/01/awesome-tensorflow-chinese/