| 000 | nam a22 4500 | ||
|---|---|---|---|
| 999 |
_c7662 _d7662 |
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| 005 | 20181115122817.0 | ||
| 008 | 181115b xxu||||| |||| 00| 0 eng d | ||
| 020 | _a9789352136100 | ||
| 040 | _cAIKTC-KRRC | ||
| 041 | _aENG | ||
| 082 |
_2DDC23 _a006.31 _bHOP/RES |
||
| 100 |
_95070 _aHope, Tom |
||
| 245 |
_aLearning tensorflow _b: A guide to building deep learning systems |
||
| 250 | _a1st | ||
| 260 |
_aSebastopol _bO'Reilly Media, Inc. _c2017 |
||
| 300 |
_ax, 228p. _bPaperback _c23*18 cm |
||
| 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 audiencefrom data scientists and engineers to students and researchers. Youll 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, youll know how to build and deploy production-ready deep learning systems in TensorFlow. | ||
| 650 | 0 |
_94622 _aComputer Engineering |
|
| 700 |
_95071 _aResheff, Yezezkel S. |
||
| 700 |
_95072 _aLieder, Itay |
||
| 942 |
_2ddc _cBK |
||