Deep Learning Cookbook

Chapter 1. Tools and Techniques

In this chapter we’ll take a look at common tools and techniques for deep learning. It’s a good chapter to read through once to get an idea of what’s what and to come back to when you need it.

We’ll start out with an overview of the different types of neural networks that are covered in this book. Most of the recipes later in the book focus on getting things done and only briefly discuss how deep neural networks are architected.

We’ll then discuss where to get data from. Tech giants like Facebook and Google have access to tremendous amounts of data to do their deep learning research, but there’s enough data out there for us to do interesting stuff too. The recipes in this book take their data from a wide range of sources.

The next part is about preprocessing of data. This is a very important area that is often overlooked. Even if you have the right network setup and you have great data, you still need to make sure that the data you have is presented in the best way to the network. You want to make it as easy as possible for the network to learn the things it needs to learn and not get distracted by other irrelevant bits in the data.

1.1 Types of Neural Networks

Throughout this chapter and indeed the book we will talk about networks and models. Network is short for neural network and refers to a stack of connected layers. You feed data in on one side and transformed data comes out on the other side. Each layer implements a mathematical operation ...