Welcome to *Deep Learning Quick Reference*! In this book, I am going to attempt to make deep learning techniques more accessible, practical, and consumable to data scientists, machine learning engineers, and software engineers who need to solve problems with deep learning. If you want to train your own deep neural network and you're stuck somewhere, there is a good chance this guide will help.

This book is hands on and is intended to be a practical guide that can help you solve your problems fast. It is primarily intended for experienced machine learning engineers and data scientists who need to use deep learning to solve a problem. Aside from this chapter, which provides some of the terminology, frameworks, and background that we will need to get started, it's not meant to be read in order. Each chapter contains a practical example, complete with code and a few best practices and safe choices. We expect you to flip to the chapter you need and get started.

This book won't go deeply into the theory of deep learning and neural networks. There are many wonderful books that can provide that background, and I highly recommend that you read at least one of them (maybe a bibliography or just recommendations). We hope to provide just enough theory and mathematical intuition to get you started.

We will cover the following topics in this chapter:

- Deep neural network architectures
- Optimization algorithms for deep learning
- Deep learning frameworks
- Building datasets for deep learning