This book is for the hobbyists, programmers, or enthusiasts who want both an introduction to TensorFlow and curated recipe examples for major machine learning algorithms. This book relies on basic knowledge of mathematics and Python programming. The major goal of this book is to introduce TensorFlow and provide well-documented examples of various machine learning algorithms. It is not within the scope of this book to delve into the specifics of mathematics, machine learning, or even programming. The best description of the book's target area is to give a gentle introduction to all three. Because of this, a reader with expertise in one area may find some parts of the book too slow and some parts too fast. Users with an extensive machine learning background may find the TensorFlow code enlightening, and users with an extensive Python programming background may find the explanations helpful. For the curious reader that would like more information on specific areas, there will be a *There's more...* section at the end of most chapters, which provides references and resources on the subject.

#### TensorFlow Machine Learning Cookbook. - Second Edition

##### By :

#### TensorFlow Machine Learning Cookbook. - Second Edition

##### By:

#### Overview of this book

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and allow you to dig deeper and gain more insights into your data than ever before.
With the help of this book, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google's machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production.
By the end of the book, you will be proficient in the field of machine intelligence using TensorFlow. You will also have good insight into deep learning and be capable of implementing machine learning algorithms in real-world scenarios.

Table of Contents (13 chapters)

Preface

Free Chapter

Getting Started with TensorFlow

The TensorFlow Way

Linear Regression

Support Vector Machines

Nearest-Neighbor Methods

Neural Networks

Natural Language Processing

Convolutional Neural Networks

Recurrent Neural Networks

Taking TensorFlow to Production

More with TensorFlow

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