AI Books to Read in 2022
Artificial intelligence (AI) is being talked about everywhere these days, and the field is increasingly impacting our lives and the world around us. This is a perfect time to better understand AI and machine learning for these reasons and more.
To help you get started, here are some AI-related books that you may want to add to your 2022 reading list – and some of them are free!
The Master Algorithm by Pedro Domingos
The Master Algorithm presents a great history and overview of machine learning and AI for a non-technical or non-expert reader.
The book really helps the reader understand the many different types of algorithmic approaches to data and computer-based learning and intelligence, along with the advantages and limitations of each algorithmic approach. It also helps develop ideas and concepts around what is needed for computers to truly exhibit human-like intelligence, which is much harder than most people realize.
The Book of Why by Judea Pearl
The Book of Why is a great and unique companion to the other books listed here. It’s unique because it presents a thorough overview of causality and concepts related to cause and effect. As the famous saying goes, correlation is not causation, and you’ll certainly understand why after reading this book.
The book is also unique and highly informative in its discussions around the shortcomings of modern AI, machine learning, and other advanced analytics techniques in terms of causality. This is a great book to expand your knowledge beyond predictive and prescriptive analytics to the important world of causality.
The Hundred-Page Machine Learning Book by Andriy Burkov
The Hundred-Page Machine Learning Book provides an excellent overview of machine learning for practitioners. The book covers most areas that a practitioner should know about, and includes an appropriate amount of theory and math without being overly technical or mathematically rigorous.
I think all practitioners should have this book on their bookshelf, and it would also benefit non-practitioners that want to take a deeper dive into all aspects of machine learning.
Machine Learning Yearning by Andrew Ng
Machine Learning Yearning is a great book for practitioners. It is similar to the hundred-page machine learning book in its broad coverage of machine learning and its application to artificial intelligence, but is written more in a how-to or cookbook style.
The book is also written in a very logical order that closely mimics the typical process that a data scientist or machine learning engineer would follow when working on an end-to-end machine learning project, along with discussing relevant key considerations and tradeoffs.
Deep learning by Ian Goodfellow et al.
Deep learning is a great book on neural networks and deep learning that provides significant technical rigor around these subjects.
It was written primarily for university students and software engineers interested in learning more about machine learning and artificial intelligence, and is definitely not for those averse to mathematics or statistics.
Neural Networks and Deep Learning by Michael Nielsen (Online Book)
Neural Networks and Deep Learning is a very easy to read and understand online book specifically about neural networks and deep learning. It includes a lot of helpful and great looking images, visualizations, and even videos.
I love the author's writing style and I think that people can learn a lot by reading this book.
Dive into Deep Learning by Multiple Authors
Dive into Deep Learning is an impressive and very comprehensive open source book on deep learning. The authors describe it as an interactive deep learning book with multi-framework code, math, and discussions. They also state that the book has been adopted at 300 universities from 55 countries including Stanford, MIT, Harvard, and Cambridge.
You’ll definitely want to check this book out if you’re looking to take a dive into the technical nuts and bolts of AI and machine learning across many areas of these fields. Visit the book’s website to learn more and peruse the book’s content, and you can also check out the book’s repository here on GitHub.
AI for People and Business by Alex Castrounis
A shameless plug given that I wrote this book, although I believe it will provide significant value to a wide-ranging audience. It’s becoming imperative for business leaders to understand artificial intelligence and machine learning at an appropriate level in order to build great data-centric products and solutions.
I wrote AI for People and Business for executives, managers, and non-technical folks interested in leveraging AI successfully within their organization, and to help readers understand the many benefits of AI for both people and business. I also wrote the book for practitioners interested in a business perspective around AI, and to provide simplified frameworks and models to help readers understand and explain complex concepts related to AI.
Conclusion
There you have it — a reading list for anyone interested in learning more about artificial intelligence and AI-related subjects in 2022.
It’s worth mentioning that at no extra cost to you and to help support our content creation, we may earn affiliate commissions from some of the links provided.
I hope you enjoy!
