Introduction

Artificial intelligence, or AI, is concerned with building systems that simulate intelligent behavior. It encompasses a wide range of approaches, including those based on logic, search, and probabilistic reasoning. Machine learning is a subset of AI that learns to make decisions by fitting mathematical models to observed data. This area has seen explosive growth and is now (incorrectly) almost synonymous with the term AI. A deep neural network (or deep network for short) is a type of machine learning model, and the process of fitting these models to data is referred to as deep learning. At the time of writing, deep networks are the most powerful and practical machine learning models and are often encountered in day-to-day life. It is commonplace to translate text to another language using a natural language processing algorithm, to search for images of a given object using a computer vision system, or to converse with a digital assistant via a speech recognition interface. All of these applications are powered by deep learning. As the title suggests, this book aims to help a reader new to this field understand the principles behind deep learning. The book is neither terribly theoretical (there are no proofs) nor extremely practical (there is almost no code). The goal is to explain the underlying ideas; after consuming this volume, the reader will be able to apply deep learning to novel situations where there is no existing recipe for success. Machine learning methods can coarsely be divided into three areas: supervised, unsupervised, and reinforcement learning. At the time of writing, the cutting-edge methods in all three areas rely on deep learning (figure 1.1). This introductory chapter describes these three areas at a high level, and this taxonomy is also loosely reflected in the book’s organization. Whether we like it or not, deep learning is poised to change our world, and this change will not all be positive. Hence, this chapter also contains a brief primer on AI ethics. We conclude with advice on how to make the most of this book.