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Awesome Deep Learning TensorFlow – Massive Collection of Resources

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A curated list of awesome TensorFlow experiments, libraries, and projects. Inspired by awesome-machine-learning.

What is TensorFlow?

TensorFlow is an open source software library for numerical computation using data flow graphs. In other words, the best way to build deep learning models.

More info here.

Table of Contents



* [TF-Unet]( – General purpose U-Network implemented in Keras for image segmentation
* [Sarus TF2 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).

Powered by TensorFlow

  • YOLO TensorFlow – Implementation of ‘YOLO : Real-Time Object Detection’
  • android-yolo – Real-time object detection on Android using the YOLO network, powered by TensorFlow.
  • Magenta – Research project to advance the state of the art in machine intelligence for music and art generation


  • TensorFlow Estimators – high-level TensorFlow API that greatly simplifies machine learning programming (originally tensorflow/skflow)
  • R Interface to TensorFlow – R interface to TensorFlow APIs, including Estimators, Keras, Datasets, etc.
  • Lattice – Implementation of Monotonic Calibrated Interpolated Look-Up Tables in TensorFlow
  • tensorflow.rb – TensorFlow native interface for ruby using SWIG
  • tflearn – Deep learning library featuring a higher-level API
  • TensorLayer – Deep learning and reinforcement learning library for researchers and engineers
  • TensorFlow-Slim – High-level library for defining models
  • TensorFrames – TensorFlow binding for Apache Spark
  • TensorForce – TensorForce: A TensorFlow library for applied reinforcement learning
  • TensorFlowOnSpark – initiative from Yahoo! to enable distributed TensorFlow with Apache Spark.
  • caffe-tensorflow – Convert Caffe models to TensorFlow format
  • keras – Minimal, modular deep learning library for TensorFlow and Theano
  • SyntaxNet: Neural Models of Syntax – A TensorFlow implementation of the models described in Globally Normalized Transition-Based Neural Networks, Andor et al. (2016)
  • keras-js – Run Keras models (tensorflow backend) in the browser, with GPU support
  • NNFlow – Simple framework allowing to read-in ROOT NTuples by converting them to a Numpy array and then use them in Google Tensorflow.
  • Sonnet – Sonnet is DeepMind’s library built on top of TensorFlow for building complex neural networks.
  • tensorpack – Neural Network Toolbox on TensorFlow focusing on training speed and on large datasets.
  • tf-encrypted – Layer on top of TensorFlow for doing machine learning on encrypted data
  • pytorch2keras – Convert PyTorch models to Keras (with TensorFlow backend) format
  • gluon2keras – Convert Gluon models to Keras (with TensorFlow backend) format
  • TensorIO – Lightweight, cross-platform library for deploying TensorFlow Lite models to mobile devices.
  • StellarGraph – Machine Learning on Graphs, a Python library for machine learning on graph-structured (network-structured) data.
  • DeepBay – High-Level Keras Complement for implement common architectures stacks, served as easy to use plug-n-play modules


  • Guild AI – Task runner and package manager for TensorFlow
  • 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.



Official announcements

Blog posts



  • Machine Learning with TensorFlow 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 by Jordi Torres, professor at UPC Barcelona Tech and a research manager and senior advisor at Barcelona Supercomputing Center
  • Deep Learning with Python – Develop Deep Learning Models on Theano and TensorFlow Using Keras by Jason Brownlee
  • TensorFlow 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 – 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 – 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 – 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.
  • Deep Learning using TensorLayer – by Hao Dong et al. This book covers both deep learning and the implmentation by using TensorFlow and TensorLayer.

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