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.
Great read with a mixture of simple and complex topics. As someone with a bare-bones understanding of tensorflow, this book to me serves as a great standalone reference for sample code. Its especially useful if you want to avoid getting lost in the labyrinth of the online tensorflow documentation. Particularly interesting are the chapters on tensorflow serving and the ability to distribute model training. One of those books you can refer again and again to as you gain a better understanding and appreciation of tensorflow.