Introduction --
Go with the flow : up and running with TensorFlow --
Understanding TensorFlow basics --
Convolution neural networks --
Text I : working with text and sequences, and TensorBoard visualization --
Text II : word vectors, advanced RNN, and embedding visualization --
TensorFlow abstractions and simplifications --
Queues, threads, and reading data --
Distributed TensorFlow --
Exporting and serving models with TensorFlow.

Roughly inspired by the human brain, deep neural networks trained with large amounts of data can solve complex tasks with unprecedented accuracy. This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for computer vision, natural language processing (NLP), speech recognition, and general predictive analytics. Authors Tom Hope, Yehezkel Resheff, and Itay Lieder provide a hands-on approach to TensorFlow fundamentals for a broad technical audience--from data scientists and engineers to students and researchers. You'll begin by working through some basic examples in TensorFlow before diving deeper into topics such as neural network architectures, TensorBoard visualization, TensorFlow abstraction libraries, and multithreaded input pipelines. Once you finish this book, you'll know how to build and deploy production-ready deep learning systems in TensorFlow