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Chapters 1–4 of this book will give you a foundational understanding of

what deep learning is, what it can achieve, and how it works. It will also make you

familiar with the canonical workflow for solving data problems using deep learn￾ing. If you aren’t already highly knowledgeable about deep learning, you should

definitely begin by reading part 1 in full before moving on to the practical appli￾cations in part 2.

What is deep learning?

This chapter covers

 High-level definitions of fundamental concepts

 Timeline of the development of machine learning

 Key factors behind deep learning’s rising 

popularity and future potential

In the past few years, artificial intelligence (AI) has been a subject of intense media

hype. Machine learning, deep learning, and AI come up in countless articles, often

outside of technology-minded publications. We’re promised a future of intelligent

chatbots, self-driving cars, and virtual assistants—a future sometimes painted in a

grim light and other times as utopian, where human jobs will be scarce and most

economic activity will be handled by robots or AI agents. For a future or current

practitioner of machine learning, it’s important to be able to recognize the signal

in the noise so that you can tell world-changing developments from overhyped

press releases. Our future is at stake, and it’s a future in which you have an active

role to play: after reading this book, you’ll be one of those who develop the AI

agents. So let’s tackle these questions: What has deep learning achieved so far?

How significant is it? Where are we headed next? Should you believe the hype?

 This chapter provides essential context around artificial intelligence, machine

learning, and deep learning.

3

4 CHAPTER 1 What is deep learning?

1.1 Artificial intelligence, machine learning, 

and deep learning

First, we need to define clearly what we’re talking about when we mention AI. What

are artificial intelligence, machine learning, and deep learning (see figure 1.1)? How

do they relate to each other?

1.1.1 Artificial intelligence

Artificial intelligence was born in the 1950s, when a handful of pioneers from the

nascent field of computer science started asking whether computers could be made to

“think”—a question whose ramifications we’re still exploring today. A concise definition of the field would be as follows: the effort to automate intellectual tasks normally performed by humans. As such, AI is a general field that encompasses machine learning and

deep learning, but that also includes many more approaches that don’t involve any

learning. Early chess programs, for instance, only involved hardcoded rules crafted by

programmers, and didn’t qualify as machine learning. For a fairly long time, many

experts believed that human-level artificial intelligence could be achieved by having

programmers handcraft a sufficiently large set of explicit rules for manipulating

knowledge. This approach is known as symbolic AI, and it was the dominant paradigm

in AI from the 1950s to the late 1980s. It reached its peak popularity during the expert

systems boom of the 1980s.

 Although symbolic AI proved suitable to solve well-defined, logical problems, such as

playing chess, it turned out to be intractable to figure out explicit rules for solving more

complex, fuzzy problems, such as image classification, speech recognition, and language translation. A new approach arose to take symbolic AI’s place: machine learning