Book Image

TensorFlow Machine Learning Cookbook - Second Edition

By : Nick McClure
Book Image

TensorFlow Machine Learning Cookbook - Second Edition

By: Nick McClure

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)

Neural Networks

In this chapter, we will introduce neural networks and how to implement them in TensorFlow. Most of the subsequent chapters will be based on neural networks, so learning how to use them in TensorFlow is very important. We will start by introducing the basic concepts of neural networking before working up to multilayer networks. In the last section, we will create a neural network that will learn how to play Tic Tac Toe.

In this chapter, we'll cover the following recipes:

  • Implementing operational gates
  • Working with gates and activation functions
  • Implementing a one-layer neural network
  • Implementing different layers
  • Using multilayer networks
  • Improving predictions of linear models
  • Learning to play Tic Tac Toe

The reader can find all of the code from this chapter online, at and at the Packt repository: https:/...