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.
Predict time series - 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 - 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.
YOLO TensorFlow ++ - TensorFlow implementation of 'YOLO: Real-Time Object Detection', with training and an actual support for real-time running on mobile devices.
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
Tools/Utilities
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.
Comparative Study of Deep Learning Software Frameworks - 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 - In this paper, we extend recently proposed Google TensorFlow for execution on large scale clusters using Message Passing Interface (MPI)
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
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.