[书籍介绍] 【2018新书】Pro Deep Learning with TensorFlow [推广有奖]

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Title: Pro Deep Learning with TensorFlow: A Mathematical Approach to Advanced Artificial Intelligence in Python
Author: Santanu Pattanayak
Length: 398 pages
Edition: 1st ed.
Language: English
Publisher: Apress
Publication Date: 2018-01-07
ISBN-10: 1484230957
ISBN-13: 9781484230954

Apress.Pro.Deep.Learning.with.TensorFlow.1484230957.rar (18.15 MB, 需要: 5 个论坛币) 本附件包括：
Apress.Pro.Deep.Learning.with.TensorFlow.1484230957.pdf
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Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own.

Pro Deep Learning with TensorFlow provides practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions. This book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures.

All of the practical aspects of deep learning that are relevant in any industry are emphasized in this book. You will be able to use the prototypes demonstrated to build new deep learning applications. The code presented in the book is available in the form of iPython notebooks and scripts which allow you to try out examples and extend them in interesting ways.

You will be equipped with the mathematical foundation and scientific knowledge to pursue research in this field and give back to the community.

What You'll Learn

Understand full stack deep learning using TensorFlow and gain a solid mathematical foundation for deep learning
Deploy complex deep learning solutions in production using TensorFlow
Carry out research on deep learning and perform experiments using TensorFlow

Who This Book Is For

Data scientists and machine learning professionals, software developers, graduate students, and open source enthusiasts

Table of Contents

Chapter 1: Mathematical Foundations
Chapter 2: Introduction to Deep-Learning Concepts and TensorFlow
Chapter 3: Convolutional Neural Networks
Chapter 4: Natural Language Processing Using Recurrent Neural Networks
Chapter 5: Unsupervised Learning with Restricted Boltzmann Machines and Auto-encoders
Chapter 6: Advanced Neural Networks