688 lines
34 KiB
HTML
688 lines
34 KiB
HTML
<h1 id="awesome-tensorflow-awesome">Awesome TensorFlow <a
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href="https://github.com/jtoy/awesome"><img
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src="https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg"
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alt="Awesome" /></a></h1>
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<p>A curated list of awesome TensorFlow experiments, libraries, and
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projects. Inspired by awesome-machine-learning.</p>
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<h2 id="what-is-tensorflow">What is TensorFlow?</h2>
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<p>TensorFlow is an open source software library for numerical
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computation using data flow graphs. In other words, the best way to
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build deep learning models.</p>
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<p>More info <a href="http://tensorflow.org">here</a>.</p>
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<h2 id="table-of-contents">Table of Contents</h2>
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<!-- MarkdownTOC depth=4 -->
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<ul>
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<li><a href="#github-tutorials">Tutorials</a></li>
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<li><a href="#github-projects">Models/Projects</a></li>
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<li><a href="#github-powered-by">Powered by TensorFlow</a></li>
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<li><a href="#libraries">Libraries</a></li>
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<li><a href="#tools-utils">Tools/Utilities</a></li>
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<li><a href="#video">Videos</a></li>
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<li><a href="#papers">Papers</a></li>
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<li><a href="#blogs">Blog posts</a></li>
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<li><a href="#community">Community</a></li>
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<li><a href="#books">Books</a></li>
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</ul>
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<!-- /MarkdownTOC -->
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<p><a name="github-tutorials" /></p>
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<h2 id="tutorials">Tutorials</h2>
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<ul>
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<li><a href="https://github.com/pkmital/tensorflow_tutorials">TensorFlow
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Tutorial 1</a> - From the basics to slightly more interesting
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applications of TensorFlow</li>
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<li><a href="https://github.com/nlintz/TensorFlow-Tutorials">TensorFlow
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Tutorial 2</a> - Introduction to deep learning based on Google’s
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TensorFlow framework. These tutorials are direct ports of Newmu’s
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Theano</li>
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<li><a
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href="https://github.com/Hvass-Labs/TensorFlow-Tutorials">TensorFlow
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Tutorial 3</a> - These tutorials are intended for beginners in Deep
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Learning and TensorFlow with well-documented code and YouTube
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videos.</li>
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<li><a
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href="https://github.com/aymericdamien/TensorFlow-Examples">TensorFlow
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Examples</a> - TensorFlow tutorials and code examples for beginners</li>
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<li><a href="https://github.com/sjchoi86/Tensorflow-101">Sungjoon’s
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TensorFlow-101</a> - TensorFlow tutorials written in Python with Jupyter
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Notebook</li>
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<li><a href="https://github.com/terryum/TensorFlow_Exercises">Terry Um’s
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TensorFlow Exercises</a> - Re-create the codes from other TensorFlow
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examples</li>
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<li><a
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href="https://github.com/samjabrahams/tensorflow-on-raspberry-pi">Installing
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TensorFlow on Raspberry Pi 3</a> - TensorFlow compiled and running
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properly on the Raspberry Pi</li>
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<li><a
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href="https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition">Classification
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on time series</a> - Recurrent Neural Network classification in
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TensorFlow with LSTM on cellphone sensor data</li>
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<li><a
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href="https://omid.al/posts/2017-02-20-Tutorial-Build-Your-First-Tensorflow-Android-App.html">Getting
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Started with TensorFlow on Android</a> - Build your first TensorFlow
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Android app</li>
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<li><a
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href="https://github.com/guillaume-chevalier/seq2seq-signal-prediction">Predict
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time series</a> - Learn to use a seq2seq model on simple datasets as an
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introduction to the vast array of possibilities that this architecture
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offers</li>
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<li><a href="https://github.com/Mazecreator/TensorFlow-SIRDS">Single
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Image Random Dot Stereograms</a> - SIRDS is a means to present 3D data
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in a 2D image. It allows for scientific data display of a waterfall type
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plot with no hidden lines due to perspective.</li>
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<li><a href="http://web.stanford.edu/class/cs20si/syllabus.html">CS20
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SI: TensorFlow for DeepLearning Research</a> - Stanford Course about
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Tensorflow from 2017 - <a
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href="http://web.stanford.edu/class/cs20si/syllabus.html">Syllabus</a> -
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<a
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href="https://youtu.be/g-EvyKpZjmQ?list=PLSPPwKHXGS2110rEaNH7amFGmaD5hsObs">Unofficial
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Videos</a></li>
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<li><a href="https://github.com/astorfi/TensorFlow-World">TensorFlow
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World</a> - Concise and ready-to-use TensorFlow tutorials with detailed
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documentation are provided.</li>
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<li><a href="https://github.com/vahidk/EffectiveTensorflow">Effective
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Tensorflow</a> - TensorFlow howtos and best practices. Covers the basics
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as well as advanced topics.</li>
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<li><a
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href="http://tensorlayer.readthedocs.io/en/latest/user/tutorial.html">TensorLayer</a>
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- Modular implementation for TensorFlow’s official tutorials. (<a
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href="https://tensorlayercn.readthedocs.io/zh/latest/user/tutorial.html">CN</a>).</li>
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<li><a
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href="https://www.lighttag.io/blog/tensorflow-estimator-api/">Understanding
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The Tensorflow Estimator API</a> A conceptual overview of the Estimator
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API, when you’d use it and why.</li>
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<li><a
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href="https://www.coursera.org/learn/introduction-tensorflow">Introduction
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to TensorFlow for Artificial Intelligence, Machine Learning, and Deep
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Learning</a> - Introduction to Tensorflow offered by Coursera</li>
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<li><a
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href="https://www.coursera.org/learn/convolutional-neural-networks-tensorflow">Convolutional
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Neural Networks in TensorFlow</a> - Convolutional Neural Networks in
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Tensorflow, offered by Coursera</li>
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<li><a
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href="https://tensorlayerx.readthedocs.io/en/latest/index.html#user-guide">TensorLayerX</a>
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- Using TensorFlow like PyTorch. (<a
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href="https://tensorlayerx.readthedocs.io/en/latest/index.html#">Api
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docs</a>)</li>
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</ul>
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<p><a name="github-projects" /></p>
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<h2 id="modelsprojects">Models/Projects</h2>
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<ul>
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<li><a
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href="https://github.com/Mrgemy95/Tensorflow-Project-Template">Tensorflow-Project-Template</a>
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- A simple and well-designed template for your tensorflow project.</li>
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<li><a href="https://github.com/yunjey/dtn-tensorflow">Domain Transfer
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Network</a> - Implementation of Unsupervised Cross-Domain Image
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Generation</li>
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<li><a href="https://github.com/yunjey/show_attend_and_tell">Show,
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Attend and Tell</a> - Attention Based Image Caption Generator</li>
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<li><a href="https://github.com/cysmith/neural-style-tf">Neural
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Style</a> Implementation of Neural Style</li>
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<li><a href="https://github.com/tensorlayer/srgan">SRGAN</a> -
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Photo-Realistic Single Image Super-Resolution Using a Generative
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Adversarial Network</li>
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<li><a href="https://github.com/google/prettytensor">Pretty Tensor</a> -
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Pretty Tensor provides a high level builder API</li>
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<li><a href="https://github.com/anishathalye/neural-style">Neural
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Style</a> - An implementation of neural style</li>
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<li><a href="https://github.com/denti/AlexNet3D">AlexNet3D</a> - An
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implementations of AlexNet3D. Simple AlexNet model but with 3D
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convolutional layers (conv3d).</li>
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<li><a
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href="https://github.com/samjabrahams/tensorflow-white-paper-notes">TensorFlow
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White Paper Notes</a> - Annotated notes and summaries of the TensorFlow
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white paper, along with SVG figures and links to documentation</li>
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<li><a
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href="https://github.com/ckmarkoh/neuralart_tensorflow">NeuralArt</a> -
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Implementation of A Neural Algorithm of Artistic Style</li>
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<li><a
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href="https://github.com/hardmaru/write-rnn-tensorflow">Generative
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Handwriting Demo using TensorFlow</a> - An attempt to implement the
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random handwriting generation portion of Alex Graves’ paper</li>
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<li><a href="https://github.com/carpedm20/NTM-tensorflow">Neural Turing
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Machine in TensorFlow</a> - implementation of Neural Turing Machine</li>
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<li><a href="https://github.com/agermanidis/thingscoop">GoogleNet
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Convolutional Neural Network Groups Movie Scenes By Setting</a> -
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Search, filter, and describe videos based on objects, places, and other
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things that appear in them</li>
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<li><a
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href="https://github.com/tokestermw/tensorflow-shakespeare">Neural
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machine translation between the writings of Shakespeare and modern
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English using TensorFlow</a> - This performs a monolingual translation,
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going from modern English to Shakespeare and vice-versa.</li>
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<li><a href="https://github.com/Conchylicultor/DeepQA">Chatbot</a> -
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Implementation of <a href="http://arxiv.org/abs/1506.05869">“A neural
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conversational model”</a></li>
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<li><a
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href="https://github.com/tensorlayer/seq2seq-chatbot">Seq2seq-Chatbot</a>
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- Chatbot in 200 lines of code</li>
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<li><a href="https://github.com/tensorlayer/dcgan">DCGAN</a> - Deep
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Convolutional Generative Adversarial Networks</li>
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<li><a href="https://github.com/zsdonghao/text-to-image">GAN-CLS</a>
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-Generative Adversarial Text to Image Synthesis</li>
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<li><a href="https://github.com/zsdonghao/Unsup-Im2Im">im2im</a> -
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Unsupervised Image to Image Translation with Generative Adversarial
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Networks</li>
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<li><a href="https://github.com/luoxier/CycleGAN_Tensorlayer">Improved
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CycleGAN</a> - Unpaired Image to Image Translation</li>
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<li><a href="https://github.com/nebulaV/DAGAN">DAGAN</a> - Fast
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Compressed Sensing MRI Reconstruction</li>
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<li><a href="https://github.com/pavelgonchar/colornet">Colornet - Neural
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Network to colorize grayscale images</a> - Neural Network to colorize
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grayscale images</li>
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<li><a
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href="https://github.com/jazzsaxmafia/show_attend_and_tell.tensorflow">Neural
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Caption Generator</a> - Implementation of <a
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href="http://arxiv.org/abs/1411.4555">“Show and Tell”</a></li>
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<li><a
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href="https://github.com/jazzsaxmafia/show_attend_and_tell.tensorflow">Neural
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Caption Generator with Attention</a> - Implementation of <a
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href="http://arxiv.org/abs/1502.03044">“Show, Attend and Tell”</a></li>
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<li><a
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href="https://github.com/jazzsaxmafia/Weakly_detector">Weakly_detector</a>
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- Implementation of <a
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href="http://cnnlocalization.csail.mit.edu/">“Learning Deep Features for
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Discriminative Localization”</a></li>
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<li><a href="https://github.com/jazzsaxmafia/dcn.tf">Dynamic Capacity
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Networks</a> - Implementation of <a
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href="http://arxiv.org/abs/1511.07838">“Dynamic Capacity
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Networks”</a></li>
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<li><a href="https://github.com/dwiel/tensorflow_hmm">HMM in
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TensorFlow</a> - Implementation of viterbi and forward/backward
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algorithms for HMM</li>
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<li><a href="https://github.com/trailbehind/DeepOSM">DeepOSM</a> - Train
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TensorFlow neural nets with OpenStreetMap features and satellite
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imagery.</li>
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<li><a
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href="https://github.com/devsisters/DQN-tensorflow">DQN-tensorflow</a> -
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TensorFlow implementation of DeepMind’s ‘Human-Level Control through
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Deep Reinforcement Learning’ with OpenAI Gym by Devsisters.com</li>
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<li><a
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href="https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_atari_pong.py">Policy
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Gradient</a> - For Playing Atari Ping Pong</li>
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<li><a
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href="https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_frozenlake_dqn.py">Deep
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Q-Network</a> - For Playing Frozen Lake Game</li>
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<li><a
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href="https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_cartpole_ac.py">AC</a>
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- Actor Critic for Playing Discrete Action space Game (Cartpole)</li>
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<li><a
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href="https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_bipedalwalker_a3c_continuous_action.py">A3C</a>
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- Asynchronous Advantage Actor Critic (A3C) for Continuous Action Space
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(Bipedal Walker)</li>
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<li><a
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href="https://github.com/zsdonghao/Imitation-Learning-Dagger-Torcs">DAGGER</a>
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- For Playing <a href="https://github.com/ugo-nama-kun/gym_torcs">Gym
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Torcs</a></li>
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<li><a href="https://github.com/jjkke88/RL_toolbox">TRPO</a> - For
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Continuous and Discrete Action Space by</li>
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<li><a href="https://github.com/fomorians/highway-cnn">Highway
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Network</a> - TensorFlow implementation of <a
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href="http://arxiv.org/abs/1507.06228">“Training Very Deep Networks”</a>
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with a <a
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href="https://medium.com/jim-fleming/highway-networks-with-tensorflow-1e6dfa667daa#.ndicn1i27">blog
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post</a></li>
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<li><a
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href="https://github.com/tqtg/hierarchical-attention-networks">Hierarchical
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Attention Networks</a> - TensorFlow implementation of <a
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href="https://www.cs.cmu.edu/~hovy/papers/16HLT-hierarchical-attention-networks.pdf">“Hierarchical
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Attention Networks for Document Classification”</a></li>
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<li><a
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href="https://github.com/dennybritz/cnn-text-classification-tf">Sentence
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Classification with CNN</a> - TensorFlow implementation of <a
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href="http://arxiv.org/abs/1408.5882">“Convolutional Neural Networks for
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Sentence Classification”</a> with a <a
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href="http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/">blog
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post</a></li>
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<li><a href="https://github.com/domluna/memn2n">End-To-End Memory
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Networks</a> - Implementation of <a
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href="http://arxiv.org/abs/1503.08895">End-To-End Memory
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Networks</a></li>
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<li><a
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href="https://github.com/carpedm20/lstm-char-cnn-tensorflow">Character-Aware
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Neural Language Models</a> - TensorFlow implementation of <a
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href="http://arxiv.org/abs/1508.06615">Character-Aware Neural Language
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Models</a></li>
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<li><a href="https://github.com/thtrieu/yolotf">YOLO TensorFlow ++</a> -
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TensorFlow implementation of ‘YOLO: Real-Time Object Detection’, with
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training and an actual support for real-time running on mobile
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devices.</li>
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<li><a href="https://github.com/ibab/tensorflow-wavenet">Wavenet</a> -
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This is a TensorFlow implementation of the <a
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href="https://deepmind.com/blog/wavenet-generative-model-raw-audio/">WaveNet
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generative neural network architecture</a> for audio generation.</li>
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<li><a href="https://github.com/trigeorgis/mdm">Mnemonic Descent
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Method</a> - Tensorflow implementation of <a
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href="http://ibug.doc.ic.ac.uk/media/uploads/documents/trigeorgis2016mnemonic.pdf">“Mnemonic
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Descent Method: A recurrent process applied for end-to-end face
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alignment”</a></li>
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<li><a href="https://github.com/InFoCusp/tf_cnnvis">CNN visualization
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using Tensorflow</a> - Tensorflow implementation of <a
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href="https://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf">“Visualizing
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and Understanding Convolutional Networks”</a></li>
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<li><a href="https://github.com/Singularity42/VGAN-Tensorflow">VGAN
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Tensorflow</a> - Tensorflow implementation for MIT <a
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href="http://carlvondrick.com/tinyvideo/">“Generating Videos with Scene
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Dynamics”</a> by Vondrick et al.</li>
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<li><a
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href="https://github.com/astorfi/3D-convolutional-speaker-recognition">3D
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Convolutional Neural Networks in TensorFlow</a> - Implementation of <a
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href="https://arxiv.org/abs/1705.09422">“3D Convolutional Neural
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Networks for Speaker Verification application”</a> in TensorFlow by
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Torfi et al.</li>
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<li><a href="https://github.com/zsdonghao/u-net-brain-tumor">U-Net</a> -
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For Brain Tumor Segmentation</li>
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<li><a
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href="https://github.com/zsdonghao/Spatial-Transformer-Nets">Spatial
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Transformer Networks</a> - Learn the Transformation Function</li>
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<li><a href="https://github.com/astorfi/lip-reading-deeplearning">Lip
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Reading - Cross Audio-Visual Recognition using 3D Architectures in
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TensorFlow</a> - TensorFlow Implementation of <a
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href="https://arxiv.org/abs/1706.05739">“Cross Audio-Visual Recognition
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in the Wild Using Deep Learning”</a> by Torfi et al.</li>
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<li><a href="https://github.com/akosiorek/hart">Attentive Object
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Tracking</a> - Implementation of <a
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href="https://arxiv.org/abs/1706.09262">“Hierarchical Attentive
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Recurrent Tracking”</a></li>
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<li><a href="https://github.com/laxatives/TensorFlow-TransX">Holographic
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Embeddings for Graph Completion and Link Prediction</a> - Implementation
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of <a href="http://arxiv.org/abs/1510.04935">Holographic Embeddings of
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Knowledge Graphs</a></li>
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<li><a
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href="https://github.com/akosiorek/attend_infer_repeat">Unsupervised
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Object Counting</a> - Implementation of <a
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href="https://papers.nips.cc/paper/6230-attend-infer-repeat-fast-scene-understanding-with-generative-models">“Attend,
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Infer, Repeat”</a></li>
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<li><a href="https://github.com/apcode/tensorflow_fasttext">Tensorflow
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FastText</a> - A simple embedding based text classifier inspired by
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Facebook’s fastText.</li>
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<li><a
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href="https://github.com/mlachmish/MusicGenreClassification">MusicGenreClassification</a>
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- Classify music genre from a 10 second sound stream using a Neural
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Network.</li>
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<li><a href="https://github.com/kubeflow/kubeflow">Kubeflow</a> -
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Framework for easily using Tensorflow with Kubernetes.</li>
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<li><a href="https://github.com/taehoonlee/tensornets">TensorNets</a> -
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40+ Popular Computer Vision Models With Pre-trained Weights.</li>
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<li><a href="https://github.com/divamgupta/ladder_network_keras">Ladder
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Network</a> - Implementation of Ladder Network for Semi-Supervised
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Learning in Keras and Tensorflow</li>
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<li><a href="https://github.com/juniorxsound/TF-Unet">TF-Unet</a> -
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General purpose U-Network implemented in Keras for image
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segmentation</li>
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<li><a href="https://github.com/sarus-tech/tf2-published-models">Sarus
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TF2 Models</a> - A long list of recent generative models implemented in
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clean, easy to reuse, Tensorflow 2 code (Plain Autoencoder, VAE, VQ-VAE,
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PixelCNN, Gated PixelCNN, PixelCNN++, PixelSNAIL, Conditional Neural
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Processes).</li>
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<li><a href="https://www.tensorflow.org/lite/guide/model_maker">Model
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Maker</a> - A transfer learning library that simplifies the process of
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training, evaluation and deployment for TensorFlow Lite models (support:
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Image Classification, Object Detection, Text Classification, BERT
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Question Answer, Audio Classification, Recommendation etc.; <a
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href="https://www.tensorflow.org/lite/api_docs/python/tflite_model_maker">API
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reference</a>).</li>
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</ul>
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<p><a name="github-powered-by" /></p>
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<h2 id="powered-by-tensorflow">Powered by TensorFlow</h2>
|
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<ul>
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<li><a href="https://github.com/gliese581gg/YOLO_tensorflow">YOLO
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TensorFlow</a> - Implementation of ‘YOLO : Real-Time Object
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Detection’</li>
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<li><a
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href="https://github.com/natanielruiz/android-yolo">android-yolo</a> -
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Real-time object detection on Android using the YOLO network, powered by
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TensorFlow.</li>
|
||
<li><a href="https://github.com/tensorflow/magenta">Magenta</a> -
|
||
Research project to advance the state of the art in machine intelligence
|
||
for music and art generation</li>
|
||
</ul>
|
||
<p><a name="libraries" /></p>
|
||
<h2 id="libraries">Libraries</h2>
|
||
<ul>
|
||
<li><a href="https://www.tensorflow.org/guide/estimators">TensorFlow
|
||
Estimators</a> - high-level TensorFlow API that greatly simplifies
|
||
machine learning programming (originally <a
|
||
href="https://github.com/tensorflow/skflow">tensorflow/skflow</a>)</li>
|
||
<li><a href="https://tensorflow.rstudio.com/">R Interface to
|
||
TensorFlow</a> - R interface to TensorFlow APIs, including Estimators,
|
||
Keras, Datasets, etc.</li>
|
||
<li><a href="https://github.com/tensorflow/lattice">Lattice</a> -
|
||
Implementation of Monotonic Calibrated Interpolated Look-Up Tables in
|
||
TensorFlow</li>
|
||
<li><a
|
||
href="https://github.com/somaticio/tensorflow.rb">tensorflow.rb</a> -
|
||
TensorFlow native interface for ruby using SWIG</li>
|
||
<li><a href="https://github.com/tflearn/tflearn">tflearn</a> - Deep
|
||
learning library featuring a higher-level API</li>
|
||
<li><a href="https://github.com/tensorlayer/tensorlayer">TensorLayer</a>
|
||
- Deep learning and reinforcement learning library for researchers and
|
||
engineers</li>
|
||
<li><a
|
||
href="https://github.com/tensorflow/models/tree/master/inception/inception/slim">TensorFlow-Slim</a>
|
||
- High-level library for defining models</li>
|
||
<li><a href="https://github.com/tjhunter/tensorframes">TensorFrames</a>
|
||
- TensorFlow binding for Apache Spark</li>
|
||
<li><a href="https://github.com/reinforceio/tensorforce">TensorForce</a>
|
||
- TensorForce: A TensorFlow library for applied reinforcement
|
||
learning</li>
|
||
<li><a
|
||
href="https://github.com/yahoo/TensorFlowOnSpark">TensorFlowOnSpark</a>
|
||
- initiative from Yahoo! to enable distributed TensorFlow with Apache
|
||
Spark.</li>
|
||
<li><a
|
||
href="https://github.com/ethereon/caffe-tensorflow">caffe-tensorflow</a>
|
||
- Convert Caffe models to TensorFlow format</li>
|
||
<li><a href="http://keras.io">keras</a> - Minimal, modular deep learning
|
||
library for TensorFlow and Theano</li>
|
||
<li><a
|
||
href="https://github.com/tensorflow/models/tree/master/syntaxnet">SyntaxNet:
|
||
Neural Models of Syntax</a> - A TensorFlow implementation of the models
|
||
described in <a href="http://arxiv.org/pdf/1603.06042.pdf">Globally
|
||
Normalized Transition-Based Neural Networks, Andor et
|
||
al. (2016)</a></li>
|
||
<li><a href="https://github.com/transcranial/keras-js">keras-js</a> -
|
||
Run Keras models (tensorflow backend) in the browser, with GPU
|
||
support</li>
|
||
<li><a href="https://github.com/welschma/NNFlow">NNFlow</a> - Simple
|
||
framework allowing to read-in ROOT NTuples by converting them to a Numpy
|
||
array and then use them in Google Tensorflow.</li>
|
||
<li><a href="https://github.com/deepmind/sonnet">Sonnet</a> - Sonnet is
|
||
DeepMind’s library built on top of TensorFlow for building complex
|
||
neural networks.</li>
|
||
<li><a href="https://github.com/ppwwyyxx/tensorpack">tensorpack</a> -
|
||
Neural Network Toolbox on TensorFlow focusing on training speed and on
|
||
large datasets.</li>
|
||
<li><a
|
||
href="https://github.com/mortendahl/tf-encrypted">tf-encrypted</a> -
|
||
Layer on top of TensorFlow for doing machine learning on encrypted
|
||
data</li>
|
||
<li><a
|
||
href="https://github.com/nerox8664/pytorch2keras">pytorch2keras</a> -
|
||
Convert PyTorch models to Keras (with TensorFlow backend) format</li>
|
||
<li><a href="https://github.com/stjordanis/gluon2keras">gluon2keras</a>
|
||
- Convert Gluon models to Keras (with TensorFlow backend) format</li>
|
||
<li><a href="https://doc-ai.github.io/tensorio/">TensorIO</a> -
|
||
Lightweight, cross-platform library for deploying TensorFlow Lite models
|
||
to mobile devices.</li>
|
||
<li><a
|
||
href="https://github.com/stellargraph/stellargraph">StellarGraph</a> -
|
||
Machine Learning on Graphs, a Python library for machine learning on
|
||
graph-structured (network-structured) data.</li>
|
||
<li><a href="https://github.com/ElPapi42/DeepBay">DeepBay</a> -
|
||
High-Level Keras Complement for implement common architectures stacks,
|
||
served as easy to use plug-n-play modules</li>
|
||
<li><a
|
||
href="https://www.tensorflow.org/probability">Tensorflow-Probability</a>
|
||
- Probabalistic programming built on TensorFlow that makes it easy to
|
||
combine probabilistic models and deep learning on modern hardware.</li>
|
||
<li><a
|
||
href="https://github.com/tensorlayer/TensorLayerX">TensorLayerX</a> -
|
||
TensorLayerX: A Unified Deep Learning Framework for All Hardwares,
|
||
Backends and OS, including TensorFlow.</li>
|
||
</ul>
|
||
<p><a name="tools-utils" /></p>
|
||
<h2 id="toolsutilities">Tools/Utilities</h2>
|
||
<ul>
|
||
<li><a
|
||
href="https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/speedster">Speedster</a>
|
||
- Automatically apply SOTA optimization techniques to achieve the
|
||
maximum inference speed-up on your hardware.</li>
|
||
<li><a href="https://guild.ai">Guild AI</a> - Task runner and package
|
||
manager for TensorFlow</li>
|
||
<li><a href="https://github.com/ml-tooling/ml-workspace">ML
|
||
Workspace</a> - 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.</li>
|
||
<li><a
|
||
href="https://github.com/radi-cho/create-tf-app">create-tf-app</a> -
|
||
Project builder command line tool for Tensorflow covering environment
|
||
management, linting, and logging.</li>
|
||
</ul>
|
||
<p><a name="video" /></p>
|
||
<h2 id="videos">Videos</h2>
|
||
<ul>
|
||
<li><a href="http://bit.ly/1OX8s8Y">TensorFlow Guide 1</a> - A guide to
|
||
installation and use</li>
|
||
<li><a href="http://bit.ly/1R27Ki9">TensorFlow Guide 2</a> -
|
||
Continuation of first video</li>
|
||
<li><a href="http://bit.ly/1TCNmEY">TensorFlow Basic Usage</a> - A guide
|
||
going over basic usage</li>
|
||
<li><a href="http://bit.ly/1L9IfJx">TensorFlow Deep MNIST for
|
||
Experts</a> - Goes over Deep MNIST</li>
|
||
<li><a href="https://www.youtube.com/watch?v=ReaxoSIM5XQ">TensorFlow
|
||
Udacity Deep Learning</a> - Basic steps to install TensorFlow for free
|
||
on the Cloud 9 online service with 1Gb of data</li>
|
||
<li><a
|
||
href="http://video.foxnews.com/v/4611174773001/why-google-wants-everyone-to-have-access-to-tensorflow/?#sp=show-clips">Why
|
||
Google wants everyone to have access to TensorFlow</a></li>
|
||
<li><a
|
||
href="http://blog.altoros.com/videos-from-tensorflow-silicon-valley-meetup-january-19-2016.html">Videos
|
||
from TensorFlow Silicon Valley Meet Up 1/19/2016</a></li>
|
||
<li><a
|
||
href="http://blog.altoros.com/videos-from-tensorflow-seattle-meetup-jan-21-2016.html">Videos
|
||
from TensorFlow Silicon Valley Meet Up 1/21/2016</a></li>
|
||
<li><a
|
||
href="https://www.youtube.com/watch?v=L8Y2_Cq2X5s&index=7&list=PLmImxx8Char9Ig0ZHSyTqGsdhb9weEGam">Stanford
|
||
CS224d Lecture 7 - Introduction to TensorFlow, 19th Apr 2016</a> -
|
||
CS224d Deep Learning for Natural Language Processing by Richard
|
||
Socher</li>
|
||
<li><a
|
||
href="https://youtu.be/GZBIPwdGtkk?list=PLBkISg6QfSX9HL6us70IBs9slFciFFa4W">Diving
|
||
into Machine Learning through TensorFlow</a> - Pycon 2016 Portland
|
||
Oregon, <a
|
||
href="https://storage.googleapis.com/amy-jo/talks/tf-workshop.pdf">Slide</a>
|
||
& <a href="https://github.com/amygdala/tensorflow-workshop">Code</a>
|
||
by Julia Ferraioli, Amy Unruh, Eli Bixby</li>
|
||
<li><a href="https://youtu.be/XYwIDn00PAo">Large Scale Deep Learning
|
||
with TensorFlow</a> - Spark Summit 2016 Keynote by Jeff Dean</li>
|
||
<li><a href="https://www.youtube.com/watch?v=vq2nnJ4g6N0">Tensorflow and
|
||
deep learning - without at PhD</a> - by Martin Görner</li>
|
||
<li><a href="https://www.youtube.com/watch?v=fTUwdXUFfI8">Tensorflow and
|
||
deep learning - without at PhD, Part 2 (Google Cloud Next ’17)</a> - by
|
||
Martin Görner</li>
|
||
<li><a href="https://youtu.be/P8MZ1Z2LHrw">Image recognition in Go using
|
||
TensorFlow</a> - by Alex Pliutau</li>
|
||
</ul>
|
||
<p><a name="papers" /></p>
|
||
<h2 id="papers">Papers</h2>
|
||
<ul>
|
||
<li><a
|
||
href="http://download.tensorflow.org/paper/whitepaper2015.pdf">TensorFlow:
|
||
Large-Scale Machine Learning on Heterogeneous Distributed Systems</a> -
|
||
This paper describes the TensorFlow interface and an implementation of
|
||
that interface that we have built at Google</li>
|
||
<li><a href="https://arxiv.org/pdf/1708.02637.pdf">TensorFlow
|
||
Estimators: Managing Simplicity vs. Flexibility in High-Level Machine
|
||
Learning Frameworks</a></li>
|
||
<li><a href="https://arxiv.org/abs/1612.04251">TF.Learn: TensorFlow’s
|
||
High-level Module for Distributed Machine Learning</a></li>
|
||
<li><a href="http://arxiv.org/abs/1511.06435">Comparative Study of Deep
|
||
Learning Software Frameworks</a> - 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</li>
|
||
<li><a href="http://arxiv.org/abs/1603.02339">Distributed TensorFlow
|
||
with MPI</a> - In this paper, we extend recently proposed Google
|
||
TensorFlow for execution on large scale clusters using Message Passing
|
||
Interface (MPI)</li>
|
||
<li><a href="http://arxiv.org/abs/1603.06042">Globally Normalized
|
||
Transition-Based Neural Networks</a> - This paper describes the models
|
||
behind <a
|
||
href="https://github.com/tensorflow/models/tree/master/syntaxnet">SyntaxNet</a>.</li>
|
||
<li><a href="https://arxiv.org/abs/1605.08695">TensorFlow: A system for
|
||
large-scale machine learning</a> - This paper describes the TensorFlow
|
||
dataflow model in contrast to existing systems and demonstrate the
|
||
compelling performance</li>
|
||
<li><a href="https://arxiv.org/abs/1707.08551">TensorLayer: A Versatile
|
||
Library for Efficient Deep Learning Development</a> - 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)</li>
|
||
</ul>
|
||
<p><a name="blogs" /></p>
|
||
<h2 id="official-announcements">Official announcements</h2>
|
||
<ul>
|
||
<li><a
|
||
href="https://googleblog.blogspot.com/2015/11/tensorflow-smarter-machine-learning-for.html">TensorFlow:
|
||
smarter machine learning, for everyone</a> - An introduction to
|
||
TensorFlow</li>
|
||
<li><a
|
||
href="http://googleresearch.blogspot.com/2016/05/announcing-syntaxnet-worlds-most.html">Announcing
|
||
SyntaxNet: The World’s Most Accurate Parser Goes Open Source</a> -
|
||
Release of SyntaxNet, “an open-source neural network framework
|
||
implemented in TensorFlow that provides a foundation for Natural
|
||
Language Understanding systems.</li>
|
||
</ul>
|
||
<h2 id="blog-posts">Blog posts</h2>
|
||
<ul>
|
||
<li><a href="http://blog.tensorflow.org/">Official Tensorflow
|
||
Blog</a></li>
|
||
<li><a href="https://archive.fo/o9asj">Why TensorFlow will change the
|
||
Game for AI</a></li>
|
||
<li><a
|
||
href="http://petewarden.com/2016/02/28/tensorflow-for-poets">TensorFlow
|
||
for Poets</a> - Goes over the implementation of TensorFlow</li>
|
||
<li><a
|
||
href="http://terrytangyuan.github.io/2016/03/14/scikit-flow-intro/">Introduction
|
||
to Scikit Flow - Simplified Interface to TensorFlow</a> - Key Features
|
||
Illustrated</li>
|
||
<li><a
|
||
href="http://terrytangyuan.github.io/2016/07/08/understand-and-build-tensorflow-estimator/">Building
|
||
Machine Learning Estimator in TensorFlow</a> - Understanding the
|
||
Internals of TensorFlow Learn Estimators</li>
|
||
<li><a
|
||
href="http://terrytangyuan.github.io/2016/08/06/tensorflow-not-just-deep-learning/">TensorFlow
|
||
- Not Just For Deep Learning</a></li>
|
||
<li><a href="https://indico.io/blog/indico-tensorflow">The indico
|
||
Machine Learning Team’s take on TensorFlow</a></li>
|
||
<li><a
|
||
href="https://indico.io/blog/the-good-bad-ugly-of-tensorflow/">The Good,
|
||
Bad, & Ugly of TensorFlow</a> - A survey of six months rapid
|
||
evolution (+ tips/hacks and code to fix the ugly stuff), Dan Kuster at
|
||
Indico, May 9, 2016</li>
|
||
<li><a
|
||
href="http://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/">Fizz Buzz
|
||
in TensorFlow</a> - A joke by Joel Grus</li>
|
||
<li><a
|
||
href="http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/">RNNs
|
||
In TensorFlow, A Practical Guide And Undocumented Features</a> -
|
||
Step-by-step guide with full code examples on GitHub.</li>
|
||
<li><a
|
||
href="http://maxmelnick.com/2016/07/04/visualizing-tensorflow-retrain.html">Using
|
||
TensorBoard to Visualize Image Classification Retraining in
|
||
TensorFlow</a></li>
|
||
<li><a
|
||
href="http://warmspringwinds.github.io/tensorflow/tf-slim/2016/12/21/tfrecords-guide/">TFRecords
|
||
Guide</a> semantic segmentation and handling the TFRecord file
|
||
format.</li>
|
||
<li><a
|
||
href="https://blog.mindorks.com/android-tensorflow-machine-learning-example-ff0e9b2654cc">TensorFlow
|
||
Android Guide</a> - Android TensorFlow Machine Learning Example.</li>
|
||
<li><a
|
||
href="https://software.intel.com/en-us/articles/tensorflow-optimizations-on-modern-intel-architecture">TensorFlow
|
||
Optimizations on Modern Intel® Architecture</a> - Introduces TensorFlow
|
||
optimizations on Intel® Xeon® and Intel® Xeon Phi™ processor-based
|
||
platforms based on an Intel/Google collaboration.</li>
|
||
<li><a
|
||
href="https://developers.googleblog.com/2017/09/how-machine-learning-with-tensorflow.html">Coca-Cola’s
|
||
Image Recognition App</a> Coca-Cola’s product code image recognizing
|
||
neural network with user input feedback loop.</li>
|
||
<li><a
|
||
href="https://www.letslearnai.com/2018/02/02/how-does-the-machine-learning-library-tensorflow-work.html">How
|
||
Does The TensorFlow Work</a> How Does The Machine Learning Library
|
||
TensorFlow Work?</li>
|
||
</ul>
|
||
<p><a name="community" /></p>
|
||
<h2 id="community">Community</h2>
|
||
<ul>
|
||
<li><a href="http://stackoverflow.com/questions/tagged/tensorflow">Stack
|
||
Overflow</a></li>
|
||
<li><a href="https://twitter.com/tensorflow"><span class="citation"
|
||
data-cites="TensorFlow">@TensorFlow</span> on Twitter</a></li>
|
||
<li><a href="https://www.reddit.com/r/tensorflow">Reddit</a></li>
|
||
<li><a
|
||
href="https://groups.google.com/a/tensorflow.org/forum/#!forum/discuss">Mailing
|
||
List</a></li>
|
||
</ul>
|
||
<p><a name="books" /></p>
|
||
<h2 id="books">Books</h2>
|
||
<ul>
|
||
<li><a href="http://tensorflowbook.com">Machine Learning with
|
||
TensorFlow</a> 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.</li>
|
||
<li><a
|
||
href="http://www.jorditorres.org/first-contact-with-tensorflow/">First
|
||
Contact with TensorFlow</a> by Jordi Torres, professor at UPC Barcelona
|
||
Tech and a research manager and senior advisor at Barcelona
|
||
Supercomputing Center</li>
|
||
<li><a
|
||
href="https://machinelearningmastery.com/deep-learning-with-python/">Deep
|
||
Learning with Python</a> - Develop Deep Learning Models on Theano and
|
||
TensorFlow Using Keras by Jason Brownlee</li>
|
||
<li><a
|
||
href="https://bleedingedgepress.com/tensor-flow-for-machine-intelligence/">TensorFlow
|
||
for Machine Intelligence</a> - Complete guide to use TensorFlow from the
|
||
basics of graph computing, to deep learning models to using it in
|
||
production environments - Bleeding Edge Press</li>
|
||
<li><a
|
||
href="https://www.packtpub.com/big-data-and-business-intelligence/getting-started-tensorflow">Getting
|
||
Started with TensorFlow</a> - Get up and running with the latest
|
||
numerical computing library by Google and dive deeper into your data, by
|
||
Giancarlo Zaccone</li>
|
||
<li><a href="http://shop.oreilly.com/product/0636920052289.do">Hands-On
|
||
Machine Learning with Scikit-Learn and TensorFlow</a> – 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).</li>
|
||
<li><a
|
||
href="https://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-projects-tensorflow">Building
|
||
Machine Learning Projects with Tensorflow</a> – 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.</li>
|
||
<li><a href="http://www.broadview.com.cn/book/5059">Deep Learning using
|
||
TensorLayer</a> - by Hao Dong et al. This book covers both deep learning
|
||
and the implmentation by using TensorFlow and TensorLayer.</li>
|
||
<li><a
|
||
href="https://www.manning.com/books/tensorflow-in-action">TensorFlow 2.0
|
||
in Action</a> - 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.</li>
|
||
<li><a
|
||
href="https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers">Probabilistic
|
||
Programming and Bayesian Methods for Hackers</a> - by Cameron
|
||
Davidson-Pilon. Introduction to Bayesian methods and probabalistic
|
||
graphical models using tensorflow-probability (and, alternatively
|
||
PyMC2/3).</li>
|
||
</ul>
|
||
<p><a name="contributions" /></p>
|
||
<h2 id="contributions">Contributions</h2>
|
||
<p>Your contributions are always welcome!</p>
|
||
<p>If you want to contribute to this list (please do), send me a pull
|
||
request or contact me <a href="https://twitter.com/jtoy"><span
|
||
class="citation" data-cites="jtoy">@jtoy</span></a> Also, if you notice
|
||
that any of the above listed repositories should be deprecated, due to
|
||
any of the following reasons:</p>
|
||
<ul>
|
||
<li>Repository’s owner explicitly say that “this library is not
|
||
maintained”.</li>
|
||
<li>Not committed for long time (2~3 years).</li>
|
||
</ul>
|
||
<p>More info on the <a
|
||
href="https://github.com/jtoy/awesome-tensorflow/blob/master/contributing.md">guidelines</a></p>
|
||
<p><a name="credits" /></p>
|
||
<h2 id="credits">Credits</h2>
|
||
<ul>
|
||
<li>Some of the python libraries were cut-and-pasted from <a
|
||
href="https://github.com/vinta/awesome-python">vinta</a></li>
|
||
<li>The few go reference I found where pulled from <a
|
||
href="https://code.google.com/p/go-wiki/wiki/Projects#Machine_Learning">this
|
||
page</a></li>
|
||
</ul>
|