Preface

Preface plan:
- What you will learn
- Who is this book for
- Software and hardware requirements
- How to read this book

TODO: introductory paragraph - Generative AI is a revolutionary new technlogy that has recently leapt from lab demos to real-world applications impacting hundreds of millions of people.

TODO: it’s hard to learn - Past approaches were very discipline-specific and required learning lots about, say, natural language. But the trend is towards finding general-purpose methods that can be applied to many different kinds of data. This is a very exciting time to be learning about generative AI, but it’s also a very challenging time. - Two big current trends: transformers and diffusion models. We’ll cover both in this book.

TODO: this book is different - We won’t be focusing on building models from scratch. Instead, we’ll be focusing on using existing models to solve real-world problems. This is a very different approach from most other books on the topic, which focus on the theory and math behind the models.

What you will learn

The book is divided into three parts: - TODO part 1 is… - TODO part 2 is… - TODO part 3 is…

How to read this book

TODO: this book is designed to be read in order, but you can also jump around to the parts that interest you most.

TODO: Maybe advice for different types of reader? EG if you’re only interested in NLP, you can skip the computer vision parts.

TODO: How to make the most of it

Pre-requisites and Software Requirements

TODO: what you need to know (not much)

TODO: talk about the tech stack and how to set it up

SoTA: A Moving Target

TODO: this field is moving very fast. We’ll do our best to keep this book up to date, but it’s a moving target.

TODO: some tips to stay up-to-date, and how to focus on general principles rather than specific models.