000			02105nam a22002177a 4500
999			_c56506 _d56506
005			20200312145249.0
008			200312b ||||| |||| 00| 0 eng d
020			_a9789352136100
028			_aRaghav Books Private Limited _bA-184 A, Nand Gram, Ghaziabad, U.P. _c1805 _d07/03/2020
082			_a006.31 HOP
100			_aTom Hope _a; Yehezkel S Resheff _a; Itay Lieder
245			_aLearning TensorFlow _b: a guide to building deep learning systems _c/Tom Hope
260			_aSebastopol, CA _b: O'Reilly Media _c, 2017.
300			_ax,228 Pages
365			_b700
505			_a 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.
520			_aRoughly 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
650			_aComputer Science
655			_aMachine learning _bArtificial intelligence
942			_cBOOK