Learning TensorFlow
A Guide to Building Deep Learning Systems

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Language: Anglais
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229 p. · Paperback
TensorFlow is currently the leading open-source software for deep learning, used by a rapidly growing number of practitioners working on computer vision, Natural Language Processing (NLP), speech recognition, and general predictive analytics. This book is an end-to-end guide to TensorFlow designed for data scientists, engineers, students and researchers.

With this book you will learn how to:
. Get up and running with TensorFlow, rapidly and painlessly
. Build and train popular deep learning models for computer vision and NLP
. Apply your advanced understanding of the TensorFlow framework to build and adapt models for your specific needs
. Train models at scale, and deploy TensorFlow in a production setting
- Preface
- Chapter 1Introduction
- Chapter 2Go with the Flow: Up and running with TensorFlow
- Chapter 3Understanding TensorFlow Basics
- Chapter 4Convolutional Neural Networks
- Chapter 5Text I: Working with text and sequences + TensorBoard visualization
- Chapter 6Text II: Word vectors, advanced RNN and embedding visualization
- Chapter 7TensorFlow abstractions and simplifications
- Chapter 8Queues, threads, and reading data
- Chapter 9Distributed TensorFlow
- Chapter 10Exporting and serving models with TensorFlow
- Appendix A
Tom Hope is an applied machine learning researcher and data scientist with extensive background in academia and industry.
He has background as a senior data scientist in large international corporation settings, leading data science and deep learning R&D across multiple domains including web mining, text analytics, computer vision,sales and marketing, IoT, financial forecasting and large-scale manufacturing.