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 Tensor Flow, 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 Tensor Flow fundamentals for a broad technical audiencefrom data scientists and engineers to students and researchers. Youll begin by working through some basic examples in Tensor Flow before diving deeper into topics such as neural network architectures, Tensor Board visualization, Tensor Flow abstraction libraries, and multithreaded input pipelines. Once you finish this book, youll know how to build and deploy production-ready deep learning systems in Tensor Flow.Get up and running with Tensor Flow, rapidly and painlessly Learn how to use Tensor Flow to build deep learning models from the ground up Train popular deep learning models for computer vision and NLPUse extensive abstraction libraries to make development easier and faster Learn how to scale Tensor Flow, and use clusters to distribute model training Deploy Tensor Flow in a production setting
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