awesome-tensorflow

TensorFlow - A curated list of dedicated resources http://tensorflow.org

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tensorflow

185,446

An Open Source Machine Learning Framework for Everyone

pytorch

82,049

Tensors and Dynamic neural networks in Python with strong GPU acceleration

CNTK

17,500

Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

mxnet

20,763

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

jax

29,761

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

Quick Overview

The jtoy/awesome-tensorflow repository is a curated list of TensorFlow resources, tools, and projects. It serves as a comprehensive collection of TensorFlow-related content, including tutorials, libraries, models, and applications. This repository aims to help developers, researchers, and enthusiasts find valuable resources for working with TensorFlow.

Pros

Extensive collection of TensorFlow resources in one place
Regularly updated with new content and contributions from the community
Well-organized into categories for easy navigation
Includes both official and community-created resources

Cons

May be overwhelming for beginners due to the large amount of information
Some links may become outdated over time
Quality of resources can vary, as it includes community contributions
Lacks detailed descriptions or reviews of individual resources

Getting Started

To use this awesome list:

Visit the repository at https://github.com/jtoy/awesome-tensorflow
Browse through the categories to find resources that interest you
Click on the links to access the resources directly
Consider starring the repository to easily find it later
If you know of a great TensorFlow resource that's not listed, consider contributing by opening a pull request

Competitor Comparisons

tensorflow

185,446

An Open Source Machine Learning Framework for Everyone

Pros of tensorflow

Official repository with the most up-to-date source code and documentation
Comprehensive codebase with full implementation of TensorFlow framework
Active development and regular updates from Google and contributors

Cons of tensorflow

Large repository size, which can be overwhelming for beginners
Requires more technical knowledge to navigate and contribute
May contain experimental features that are not yet stable

Code comparison

tensorflow:

import tensorflow as tf

model = tf.keras.Sequential([
  tf.keras.layers.Dense(64, activation='relu'),
  tf.keras.layers.Dense(10, activation='softmax')
])

awesome-tensorflow:

# Awesome TensorFlow

A curated list of awesome TensorFlow experiments, libraries, and projects.

## What is TensorFlow?
TensorFlow is an open source software library for numerical computation...

Summary

tensorflow is the official repository for the TensorFlow framework, offering the complete source code and documentation. It's actively maintained by Google and the community, providing the most up-to-date features and improvements.

awesome-tensorflow, on the other hand, is a curated list of TensorFlow resources, projects, and experiments. It serves as a valuable reference for developers looking for TensorFlow-related content but doesn't contain the actual framework code.

While tensorflow is more suitable for those who want to work directly with the framework or contribute to its development, awesome-tensorflow is an excellent starting point for discovering TensorFlow projects and learning resources.

keras

61,580

Deep Learning for humans

Pros of Keras

Actual deep learning framework with full implementation
Extensive documentation and tutorials for users
Large community and ecosystem of extensions/plugins

Cons of Keras

More complex to set up and use for beginners
Larger codebase to navigate and understand
May have performance overhead compared to raw TensorFlow

Code Comparison

Keras:

from keras.models import Sequential
from keras.layers import Dense

model = Sequential([
    Dense(32, input_shape=(784,), activation='relu'),
    Dense(10, activation='softmax')
])

Awesome TensorFlow:

# No direct code implementation
# Repository is a curated list of TensorFlow resources

Summary

Keras is a full-fledged deep learning framework built on top of TensorFlow, offering a high-level API for building and training neural networks. It provides a complete ecosystem for deep learning development.

Awesome TensorFlow, on the other hand, is a curated list of TensorFlow resources, tutorials, and tools. It doesn't provide any direct implementation but serves as a valuable reference for TensorFlow users.

While Keras offers a more comprehensive solution for deep learning projects, Awesome TensorFlow provides a collection of resources that can be beneficial for both beginners and experienced TensorFlow users looking for specific tools or tutorials.

pytorch

82,049

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Pros of PyTorch

More dynamic and flexible computational graph
Easier debugging and intuitive Python-like coding style
Stronger community support and faster-growing ecosystem

Cons of PyTorch

Slightly steeper learning curve for beginners
Fewer pre-trained models and tools compared to TensorFlow
Less support for production deployment and mobile/embedded devices

Code Comparison

PyTorch:

import torch

x = torch.tensor([1, 2, 3])
y = torch.tensor([4, 5, 6])
z = torch.add(x, y)

Awesome-TensorFlow (TensorFlow):

import tensorflow as tf

x = tf.constant([1, 2, 3])
y = tf.constant([4, 5, 6])
z = tf.add(x, y)

Summary

PyTorch offers a more dynamic and flexible approach to deep learning, with easier debugging and a Python-like coding style. It has gained significant popularity and community support in recent years. However, it may have a steeper learning curve for beginners and fewer pre-trained models compared to TensorFlow.

Awesome-TensorFlow, being a curated list of TensorFlow resources, provides access to a wide range of tools, models, and tutorials. TensorFlow itself offers better support for production deployment and mobile/embedded devices but may be less intuitive for some developers.

Both frameworks are powerful and widely used in the deep learning community, with the choice often depending on specific project requirements and personal preferences.

CNTK

17,500

Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

Pros of CNTK

Developed and maintained by Microsoft, ensuring enterprise-level support and reliability
Offers better performance for recurrent neural networks (RNNs) and long short-term memory (LSTM) networks
Provides native support for distributed training across multiple GPUs and machines

Cons of CNTK

Smaller community and ecosystem compared to TensorFlow
Less extensive documentation and tutorials available
Steeper learning curve for beginners in deep learning

Code Comparison

CNTK example:

import cntk as C

x = C.input_variable(2)
y = C.layers.Dense(1)(x)
z = C.sigmoid(y)

model = C.train.Trainer(z, (y, z), C.sgd(z.parameters, 0.1))

awesome-tensorflow example:

import tensorflow as tf

x = tf.placeholder(tf.float32, shape=[None, 2])
y = tf.layers.dense(x, 1)
z = tf.sigmoid(y)

model = tf.train.GradientDescentOptimizer(0.1).minimize(z)

Note: awesome-tensorflow is not a framework but a curated list of TensorFlow resources. The code example provided is for TensorFlow itself.

mxnet

20,763

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Pros of MXNet

Full-fledged deep learning framework with comprehensive documentation
Supports multiple programming languages (Python, C++, R, Julia, etc.)
Highly scalable for distributed training on multiple GPUs/machines

Cons of MXNet

Steeper learning curve compared to curated resource lists
Requires more setup and configuration for beginners
Less focused on TensorFlow-specific resources and tutorials

Code Comparison

MXNet example:

import mxnet as mx
from mxnet import nd, autograd, gluon

# Define and train a simple neural network
net = gluon.nn.Sequential()
net.add(gluon.nn.Dense(10, activation='relu'))
net.add(gluon.nn.Dense(2))
net.initialize()

trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})

Awesome TensorFlow (no code, as it's a curated list):

# Awesome TensorFlow

A curated list of awesome TensorFlow experiments, libraries, and projects.

## What is TensorFlow?
TensorFlow is an open source software library for numerical computation...

## Contents
- [Tutorials](#tutorials)
- [Models/Projects](#models-projects)
- [Libraries](#libraries)

jax

29,761

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

Pros of JAX

More flexible and composable than TensorFlow, allowing for easier customization of models and training loops
Faster compilation and execution times, especially for complex models and large datasets
Better support for automatic differentiation and vectorization

Cons of JAX

Smaller ecosystem and fewer pre-built models compared to TensorFlow's extensive library
Steeper learning curve for developers not familiar with functional programming concepts
Less comprehensive documentation and tutorials for beginners

Code Comparison

JAX:

import jax.numpy as jnp
from jax import grad, jit

def loss(params, x, y):
    return jnp.sum((params[0] * x + params[1] - y) ** 2)

grad_loss = jit(grad(loss))

TensorFlow:

import tensorflow as tf

def loss(params, x, y):
    return tf.reduce_sum(tf.square(params[0] * x + params[1] - y))

grad_loss = tf.function(lambda p, x, y: tf.gradients(loss(p, x, y), p))

Both examples show a simple linear regression loss function and its gradient computation. JAX's syntax is more concise and uses NumPy-like operations, while TensorFlow relies on its own operations and requires explicit function decoration for optimization.

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README

Awesome TensorFlow

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.

Tutorials
Models/Projects
Powered by TensorFlow
Libraries
Tools/Utilities
Videos
Papers
Blog posts
Community
Books

Tutorials

TensorFlow Tutorial 1 - From the basics to slightly more interesting applications of TensorFlow
TensorFlow Tutorial 2 - Introduction to deep learning based on Google's TensorFlow framework. These tutorials are direct ports of Newmu's Theano
TensorFlow Tutorial 3 - These tutorials are intended for beginners in Deep Learning and TensorFlow with well-documented code and YouTube videos.
TensorFlow Examples - TensorFlow tutorials and code examples for beginners
Sungjoon's TensorFlow-101 - TensorFlow tutorials written in Python with Jupyter Notebook
Terry Umâs TensorFlow Exercises - Re-create the codes from other TensorFlow examples
Installing TensorFlow on Raspberry Pi 3 - TensorFlow compiled and running properly on the Raspberry Pi
Classification on time series - Recurrent Neural Network classification in TensorFlow with LSTM on cellphone sensor data
Getting Started with TensorFlow on Android - Build your first TensorFlow Android app
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.
CS20 SI: TensorFlow for DeepLearning Research - Stanford Course about Tensorflow from 2017 - Syllabus - Unofficial Videos
TensorFlow World - Concise and ready-to-use TensorFlow tutorials with detailed documentation are provided.
Effective Tensorflow - TensorFlow howtos and best practices. Covers the basics as well as advanced topics.
TensorLayer - Modular implementation for TensorFlow's official tutorials. (CN).
Understanding The Tensorflow Estimator API A conceptual overview of the Estimator API, when you'd use it and why.
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning - Introduction to Tensorflow offered by Coursera
Convolutional Neural Networks in TensorFlow - Convolutional Neural Networks in Tensorflow, offered by Coursera
TensorLayerX - Using TensorFlow like PyTorch. (Api docs)

Models/Projects

Tensorflow-Project-Template - A simple and well-designed template for your tensorflow project.
Domain Transfer Network - Implementation of Unsupervised Cross-Domain Image Generation
Show, Attend and Tell - Attention Based Image Caption Generator
Neural Style Implementation of Neural Style
SRGAN - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Pretty Tensor - Pretty Tensor provides a high level builder API
Neural Style - An implementation of neural style
AlexNet3D - An implementations of AlexNet3D. Simple AlexNet model but with 3D convolutional layers (conv3d).
TensorFlow White Paper Notes - Annotated notes and summaries of the TensorFlow white paper, along with SVG figures and links to documentation
NeuralArt - Implementation of A Neural Algorithm of Artistic Style
Generative Handwriting Demo using TensorFlow - An attempt to implement the random handwriting generation portion of Alex Graves' paper
Neural Turing Machine in TensorFlow - implementation of Neural Turing Machine
GoogleNet Convolutional Neural Network Groups Movie Scenes By Setting - 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 - This performs a monolingual translation, going from modern English to Shakespeare and vice-versa.
Chatbot - Implementation of "A neural conversational model"
Seq2seq-Chatbot - Chatbot in 200 lines of code
DCGAN - Deep Convolutional Generative Adversarial Networks
GAN-CLS -Generative Adversarial Text to Image Synthesis
im2im - Unsupervised Image to Image Translation with Generative Adversarial Networks
Improved CycleGAN - Unpaired Image to Image Translation
DAGAN - Fast Compressed Sensing MRI Reconstruction
Colornet - Neural Network to colorize grayscale images - Neural Network to colorize grayscale images
Neural Caption Generator - Implementation of "Show and Tell"
Neural Caption Generator with Attention - Implementation of "Show, Attend and Tell"
Weakly_detector - Implementation of "Learning Deep Features for Discriminative Localization"
Dynamic Capacity Networks - Implementation of "Dynamic Capacity Networks"
HMM in TensorFlow - Implementation of viterbi and forward/backward algorithms for HMM
DeepOSM - Train TensorFlow neural nets with OpenStreetMap features and satellite imagery.
DQN-tensorflow - TensorFlow implementation of DeepMind's 'Human-Level Control through Deep Reinforcement Learning' with OpenAI Gym by Devsisters.com
Policy Gradient - For Playing Atari Ping Pong
Deep Q-Network - For Playing Frozen Lake Game
AC - Actor Critic for Playing Discrete Action space Game (Cartpole)
A3C - Asynchronous Advantage Actor Critic (A3C) for Continuous Action Space (Bipedal Walker)
DAGGER - For Playing Gym Torcs
TRPO - For Continuous and Discrete Action Space by
Highway Network - TensorFlow implementation of "Training Very Deep Networks" with a blog post
Hierarchical Attention Networks - TensorFlow implementation of "Hierarchical Attention Networks for Document Classification"
Sentence Classification with CNN - TensorFlow implementation of "Convolutional Neural Networks for Sentence Classification" with a blog post
End-To-End Memory Networks - Implementation of End-To-End Memory Networks
Character-Aware Neural Language Models - TensorFlow implementation of Character-Aware Neural Language Models
YOLO TensorFlow ++ - TensorFlow implementation of 'YOLO: Real-Time Object Detection', with training and an actual support for real-time running on mobile devices.
Wavenet - This is a TensorFlow implementation of the WaveNet generative neural network architecture for audio generation.
Mnemonic Descent Method - Tensorflow implementation of "Mnemonic Descent Method: A recurrent process applied for end-to-end face alignment"
CNN visualization using Tensorflow - Tensorflow implementation of "Visualizing and Understanding Convolutional Networks"
VGAN Tensorflow - Tensorflow implementation for MIT "Generating Videos with Scene Dynamics" by Vondrick et al.
3D Convolutional Neural Networks in TensorFlow - Implementation of "3D Convolutional Neural Networks for Speaker Verification application" in TensorFlow by Torfi et al.
U-Net - For Brain Tumor Segmentation
Spatial Transformer Networks - Learn the Transformation Function
Lip Reading - Cross Audio-Visual Recognition using 3D Architectures in TensorFlow - TensorFlow Implementation of "Cross Audio-Visual Recognition in the Wild Using Deep Learning" by Torfi et al.
Attentive Object Tracking - Implementation of "Hierarchical Attentive Recurrent Tracking"
Holographic Embeddings for Graph Completion and Link Prediction - Implementation of Holographic Embeddings of Knowledge Graphs
Unsupervised Object Counting - Implementation of "Attend, Infer, Repeat"
Tensorflow FastText - A simple embedding based text classifier inspired by Facebook's fastText.
MusicGenreClassification - Classify music genre from a 10 second sound stream using a Neural Network.
Kubeflow - Framework for easily using Tensorflow with Kubernetes.
TensorNets - 40+ Popular Computer Vision Models With Pre-trained Weights.
Ladder Network - Implementation of Ladder Network for Semi-Supervised Learning in Keras and Tensorflow
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).
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).

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

Libraries

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
Tensorflow-Probability - Probabalistic programming built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware.
TensorLayerX - TensorLayerX: A Unified Deep Learning Framework for All Hardwares, Backends and OS, including TensorFlow.

Tools/Utilities

Speedster - Automatically apply SOTA optimization techniques to achieve the maximum inference speed-up on your hardware.
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.
create-tf-app - Project builder command line tool for Tensorflow covering environment management, linting, and logging.

Videos

TensorFlow Guide 1 - A guide to installation and use
TensorFlow Guide 2 - Continuation of first video
TensorFlow Basic Usage - A guide going over basic usage
TensorFlow Deep MNIST for Experts - Goes over Deep MNIST
TensorFlow Udacity Deep Learning - 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
Videos from TensorFlow Silicon Valley Meet Up 1/19/2016
Videos from TensorFlow Silicon Valley Meet Up 1/21/2016
Stanford CS224d Lecture 7 - Introduction to TensorFlow, 19th Apr 2016 - CS224d Deep Learning for Natural Language Processing by Richard Socher
Diving into Machine Learning through TensorFlow - Pycon 2016 Portland Oregon, Slide & Code by Julia Ferraioli, Amy Unruh, Eli Bixby
Large Scale Deep Learning with TensorFlow - Spark Summit 2016 Keynote by Jeff Dean
Tensorflow and deep learning - without at PhD - by Martin GÃ¶rner
Tensorflow and deep learning - without at PhD, Part 2 (Google Cloud Next '17) - by Martin GÃ¶rner
Image recognition in Go using TensorFlow - by Alex Pliutau

Papers

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems - This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks
TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning
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)
Globally Normalized Transition-Based Neural Networks - This paper describes the models behind SyntaxNet.
TensorFlow: A system for large-scale machine learning - This paper describes the TensorFlow dataflow model in contrast to existing systems and demonstrate the compelling performance
TensorLayer: A Versatile Library for Efficient Deep Learning Development - 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)

Official announcements

TensorFlow: smarter machine learning, for everyone - An introduction to TensorFlow
Announcing SyntaxNet: The Worldâs Most Accurate Parser Goes Open Source - Release of SyntaxNet, "an open-source neural network framework implemented in TensorFlow that provides a foundation for Natural Language Understanding systems.

Blog posts

Official Tensorflow Blog
Why TensorFlow will change the Game for AI
TensorFlow for Poets - Goes over the implementation of TensorFlow
Introduction to Scikit Flow - Simplified Interface to TensorFlow - Key Features Illustrated
Building Machine Learning Estimator in TensorFlow - Understanding the Internals of TensorFlow Learn Estimators
TensorFlow - Not Just For Deep Learning
The indico Machine Learning Team's take on TensorFlow
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 - A joke by Joel Grus
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
TFRecords Guide semantic segmentation and handling the TFRecord file format.
TensorFlow Android Guide - Android TensorFlow Machine Learning Example.
TensorFlow Optimizations on Modern IntelÂ® Architecture - Introduces TensorFlow optimizations on IntelÂ® XeonÂ® and IntelÂ® Xeon Phiâ¢ processor-based platforms based on an Intel/Google collaboration.
Coca-Cola's Image Recognition App Coca-Cola's product code image recognizing neural network with user input feedback loop.
How Does The TensorFlow Work How Does The Machine Learning Library TensorFlow Work?

Community

Books

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 â by AurÃ©lien 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 â 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.
TensorFlow 2.0 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.
Probabilistic Programming and Bayesian Methods for Hackers - by Cameron Davidson-Pilon. Introduction to Bayesian methods and probabalistic graphical models using tensorflow-probability (and, alternatively PyMC2/3).

Contributions

Your contributions are always welcome!

If you want to contribute to this list (please do), send me a pull request or contact me @jtoy Also, if you notice that any of the above listed repositories should be deprecated, due to any of the following reasons:

Repository's owner explicitly say that "this library is not maintained".
Not committed for long time (2~3 years).

More info on the guidelines

Credits

Some of the python libraries were cut-and-pasted from vinta
The few go reference I found where pulled from this page

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Quick Overview

Pros

Cons

Getting Started

Competitor Comparisons

Pros of tensorflow

Cons of tensorflow

Code comparison

Summary

Pros of Keras

Cons of Keras

Code Comparison

Summary

Pros of PyTorch

Cons of PyTorch

Code Comparison

Summary

Pros of CNTK

Cons of CNTK

Code Comparison

Pros of MXNet

Cons of MXNet

Code Comparison

Pros of JAX

Cons of JAX

Code Comparison

Convert designs to code with AI

README

Awesome TensorFlow

What is TensorFlow?

Table of Contents

Tutorials

Models/Projects

Powered by TensorFlow

Libraries

Tools/Utilities

Videos

Papers

Official announcements

Blog posts

Community

Books

Contributions

Credits

Top Related Projects

Convert designs to code with AI