1 What is deep learning?

Throughout the past decade, 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 practitioner of machine learning, it’s important to be able to recognize the signal amid the noise, so that you can tell world-changing developments from overhyped press releases. Our future is at stake, and it’s a one in which you have an active role to play: after reading this book, you’ll be one of those who can develop those AI systems. 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?

1.1 Artificial intelligence, machine learning, 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? How do they relate to each other?

1.2 Artificial intelligence

1.3 Machine learning

1.4 Learning rules and representations from data

1.5 The “deep” in “deep learning”

1.6 Understanding how deep learning works, in three figures

1.7 What makes deep learning different

1.8 The age of generative AI

1.9 What deep learning has achieved so far

1.10 Beware of the short-term hype

1.11 Summer can turn to winter

1.12 The promise of AI