Preface

This book is intended, dear reader, to show you a wide variety of practical AI techniques and examples, and to be a jumping off point when you discover things that interest you or may be useful in your work. A common theme here is covering AI programming tasks that used to be difficult or impossible but are now much simpler using modern technology. I also cover a wide variety of non-deep learning material including a chapter on Symbolic AI that has historic interest and some current practical value.

This book is meant to be a quick four to five hour introduction to AI for Python programmers. If you have experience with Large Language Models, Deep Learning, general Machine Leaning and Symbolic AI then you can spend a couple of hours experimenting with the examples.

This book is not intended as a textbook that is to be read start to finish. The parts or even chapters of this book can be read in any order. I have tried making each chapter independent and able to stand on its own even when I am building on earlier themes in the book.

My career developing AI applications and tools began in 1982. Until the advent of breakthroughs in deep learning around 2010 most of my development work was in Common Lisp, Java, and C++. My language preference changed when I started spending most of my time creating deep learning models. Python has the most tooling, libraries, and frameworks for deep learning so as a practical matter I have adopted Python as a primary programming language. That said I still also heavily use Common Lisp, Haskell, Swift, and Scheme. I recommend not having an “always use one programming language” mindset.

Why this book? Some of what I cover here has already been covered in the Common Lisp, Java, Clojure and Haskell artificial intelligence books I have previously written. My goal here is to prioritize more material material for Python developers.

Why Python? Python is a very high level language that is easily readable by other programmers. Since Python is one of the most popular programming languages there are many available libraries and frameworks. The best code is code that we don’t have to write ourselves as long as third party code is open source so we can read and modify it if needed. Another reason to use Python, that we lean heavily on in this book, is using pre-trained deep learning models that are wrapped into Python packages and libraries.

I have written over 20 books, I have over 50 US patents, and I have worked at interesting companies like Google, Capital One, SAIC, Mind AI, and others. You can read all of my recent books (including this book) for free on my web site https://markwatson.com. If I had to summarize my career the short take would be that I have had a lot of fun and enjoyed my work. I hope that what you learn here will be both enjoyable and help you in your work.

If you would like to support my work please consider purchasing my books on Leanpub and star my git repositories that you find useful on GitHub. You can also interact with me on social media on Mastodon and Twitter.

The example code that I have written for this book is Apache 2 licensed so feel free to reuse it. I also use several existing open source packages and libraries in the examples that use liberal-use licenses (I link GitHub repositories, so check the licenses for applicability in your projects).

The examples for this book (and the entire book manuscript!) are in the GitHub repository https://github.com/mark-watson/PythonPracticalAIBook. The code examples are in the directory source-code.

A few of the examples use APIs from Google and OpenAI’s GPT-4. I assume that you have signed up an have access keys that should be available in the environment variables GOOGLE_API_KEY and OPENAI_API_KEY. If you don’t want to sign up for these services I still hope that you enjoy reading the sample code and example output.

I used to live in Sedona Arizona. I have been fortunate to have visited close to one hundred ancient Native American Indian sites in the Verde Valley. I took the cover picture at one of these sites.

This picture shows me and my wife Carol who helps me with book production.

I would like to thank my wife Carol Watson who edits all of my books.

I would like to thank the following readers who reported errors or typos in this book: Ryan O’Connor (typo).