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@@ -153,8 +153,9 @@ More info [here](http://tensorflow.org).
* [TensorIO](https://doc-ai.github.io/tensorio/) - Lightweight, cross-platform library for deploying TensorFlow Lite models to mobile devices.
* [StellarGraph](https://github.com/stellargraph/stellargraph) - Machine Learning on Graphs, a Python library for machine learning on graph-structured (network-structured) data.
* [DeepBay](https://github.com/ElPapi42/DeepBay) - High-Level Keras Complement for implement common architectures stacks, served as easy to use plug-n-play modules
* [Tensorflow-Probability](https://www.tensorflow.org/probability) - Probabalistic programming built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware.
* [Tensorflow-Probability](https://www.tensorflow.org/probability) - Probabilistic programming built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware.
* [TensorLayerX](https://github.com/tensorlayer/TensorLayerX) - TensorLayerX: A Unified Deep Learning Framework for All Hardwares, Backends and OS, including TensorFlow.
* [Txeo](https://github.com/rdabra/txeo) - A modern C++ wrapper for TensorFlow.
<a name="tools-utils" />
@@ -246,9 +247,9 @@ More info [here](http://tensorflow.org).
* [Getting Started with TensorFlow](https://www.packtpub.com/big-data-and-business-intelligence/getting-started-tensorflow) - Get up and running with the latest numerical computing library by Google and dive deeper into your data, by Giancarlo Zaccone
* [Hands-On Machine Learning with Scikit-Learn and TensorFlow](http://shop.oreilly.com/product/0636920052289.do) – by Aurélien Geron, former lead of the YouTube video classification team. Covers ML fundamentals, training and deploying deep nets across multiple servers and GPUs using TensorFlow, the latest CNN, RNN and Autoencoder architectures, and Reinforcement Learning (Deep Q).
* [Building Machine Learning Projects with Tensorflow](https://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-projects-tensorflow) – by Rodolfo Bonnin. This book covers various projects in TensorFlow that expose what can be done with TensorFlow in different scenarios. The book provides projects on training models, machine learning, deep learning, and working with various neural networks. Each project is an engaging and insightful exercise that will teach you how to use TensorFlow and show you how layers of data can be explored by working with Tensors.
* [Deep Learning using TensorLayer](http://www.broadview.com.cn/book/5059) - by Hao Dong et al. This book covers both deep learning and the implmentation by using TensorFlow and TensorLayer.
* [Deep Learning using TensorLayer](http://www.broadview.com.cn/book/5059) - by Hao Dong et al. This book covers both deep learning and the implementation by using TensorFlow and TensorLayer.
* [TensorFlow 2.0 in Action](https://www.manning.com/books/tensorflow-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](https://github.com/CamDavidsonPilon/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).
* [Probabilistic Programming and Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - by Cameron Davidson-Pilon. Introduction to Bayesian methods and probabilistic graphical models using tensorflow-probability (and, alternatively PyMC2/3).
@@ -274,3 +275,6 @@ More info on the [guidelines](https://github.com/jtoy/awesome-tensorflow/blob/ma
* Some of the python libraries were cut-and-pasted from [vinta](https://github.com/vinta/awesome-python)
* The few go reference I found where pulled from [this page](https://code.google.com/p/go-wiki/wiki/Projects#Machine_Learning)
[tensorflow.md Github](https://github.com/jtoy/awesome-tensorflow
)