TensorFlow For Machine Intelligence: A hands-on introduction to learning algorithms

This book is a hands-on introduction to learning algorithms. It is for people who may know a little machine learning (or not) and who may have heard about TensorFlow, but found the documentation too daunting to approach. The learning curve is gentle and you always have some code to illustrate the math step-by-step.

TensorFlow, a popular library for machine learning, embraces the innovation and community-engagement of open source, but has the support, guidance, and stability of a large corporation. Because of its multitude of strengths, TensorFlow is appropriate for individuals and businesses ranging from startups to companies as large as, well, Google. TensorFlow is currently being used for natural language processing, artificial intelligence, computer vision, and predictive analytics.

TensorFlow, open sourced to the public by Google in November 2015, was made to be flexible, efficient, extensible, and portable. Computers of any shape and size can run it, from smartphones all the way up to huge computing clusters. This book starts with the absolute basics of TensorFlow. We found that most tutorials on TensorFlow start by attempting to teach both machine learning concepts and TensorFlow terminology at the same time. Here we first make sure you’ve had the opportunity to become comfortable with TensorFlow’s mechanics and core API before covering machine learning concepts.

Table of Contents

Part I. Getting started with TensorFlow
Chapter 1. Introduction
Chapter 2. TensorFlow Installation

Part II. TensorFlow and Machine Learning fundamentals
Chapter 3. TensorFlow Fundamentals
Chapter 4. Machine Learning Basics

Part III. Implementing Advanced Deep Models in TensorFlow
Chapter 5. Object Recognition and Classification
Chapter 6. Recurrent Neural Networks and Natural Language Processing

Part IV. Additional Tips, Techniques, and Features
Chapter 7. Deploying Models in Production
Chapter 8. Helper Functions, Code Structure, and Classes
Chapter 9. Conclusion