Chapter 1, TensorFlow – Basic Concepts, contains general information on the structure of TensorFlow and the issues for which it was developed. It also provides the basic programming guidelines for the Python language and a first TensorFlow working session after the installation procedure. The chapter ends with a description of TensorBoard, a powerful tool for optimization and debugging.

Chapter 2, Doing Math with TensorFlow, describes the ability of mathematical processing of TensorFlow. It covers programming examples on basic algebra up to partial differential equations. Also, the basic data structure in TensorFlow, the tensor, is explained.

Chapter 3, Starting with Machine Learning, introduces some machine learning models. We start to implement the linear regression algorithm, which is concerned with modeling relationships between data. The main focus of the chapter is on solving two basic problems in machine learning; classification, that is, how to assign each new input to one of the possible given categories; and data clustering, which is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.

Chapter 4, Introducing Neural Networks, provides a quick and detailed introduction of neural networks. These are mathematical models that represent the interconnection between elements, the artificial neurons. They are mathematical constructs that to some extent mimic the properties of living neurons. Neural networks build the foundation on which rests the architecture of deep learning algorithms. Two basic types of neural nets are then implemented: the Single Layer Perceptron and the Multi Layer Perceptron for classification problems.

Chapter 5, Deep Learning, gives an overview of deep learning algorithms. Only in recent years has deep learning collected a large number of results considered unthinkable a few years ago. We’ll show how to implement two fundamental deep learning architectures, convolutional neural networks (CNN) and recurrent neural networks (RNN), for image recognition and speech translation problems  respectively.

Chapter 6, GPU Programming and Serving with TensorFlow, shows the TensorFlow facilities for GPU computing and introduces TensorFlow Serving, a high-performance open source serving system for machine learning models designed for production environments and optimized for TensorFlow.

All the examples have been implemented using Python version 2.7 on an Ubuntu Linux 64-bit machine, including the TensorFlow library version 0.7.1.

You will also need the following Python modules (preferably the latest version):

The reader should have a basic knowledge of programming and math concepts, and at the same time, want to be introduced to the topics of machine and deep learning. After reading this book, you will be able to master TensorFlow’s features to build powerful applications.

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, path names, dummy URLs, user input, and Twitter handles are shown as follows: "The instructions for flow control are if, for, and while."

Any command-line input or output is written as follows:

>>> myvar = 3
>>> myvar += 2
>>> myvar
5
>>> myvar -= 1
>>> myvar
4

New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "The shortcuts in this book are based on the Mac OS X 10.5+ scheme."

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