Essential Math for AI: Next-Level Mathematics for Efficient and Successful AI Systems

by Hala Nelson

All the math we need to get into AI. Math and AI made easy...
Many industries are eager to integrate AI and data-driven technologies into their systems and operations. But to build truly successful AI systems, you need a firm grasp of the underlying mathematics. This comprehensive guide bridges the gap in presentation between the potential and applications of AI and its relevant mathematical foundations. 
In an immersive and conversational style, the book surveys the mathematics necessary to thrive in the AI field, focusing on real-world applications and state-of-the-art models, rather than on dense academic theory. You'll explore topics such as regression, neural networks, convolution, optimization, probability, graphs, random walks, Markov processes, differential equations, and more within an exclusive AI context geared toward computer vision, natural language processing, generative models, reinforcement learning, operations research, and automated systems. With a broad audience in mind, including engineers, data scientists, mathematicians, scientists, and people early in their careers, the book helps build a solid foundation for success in the AI and math fields. 
You'll be able
 

Genres: Mathematics, Artificial Intelligence, Technology, Computer Science

552 pages, Paperback

Published February 14, 2023

Reviews

[Jeevan · Rating 4 out of 5]

This was a tough read, I could grok maybe 50% of the content. However in the parts I understood, the explanations of concepts was done well. This book references many current events and research at the time of publishing, so it will be interesting to see how the examples change with future editions. It would have been good to include references by chapter to explore the topics independently. The included references are good, but are still of a level higher than what a beginner would need understanding the concepts.

I hope to get back to re-read some of the chapters in the future.

[Dan · Rating 3 out of 5]

Content a bit sloppy, but the real issue was just how poorly formatted the physical copy was.

[Tim · Rating 4 out of 5]

Mixed feelings about this one. It truly covers an enormous breadth of math and gives a solid introduction to some key concepts like convolutions, SVD, operations research, and (some) probability, but many concepts are introduced at such a high level that I had to look up other explanations to understand them. Others are just mentioned without a definition. I found it useful to get a lay of the land for what math is out there, but it isn't detailed enough about many concepts. Also some of it is relatively basic ML or CS (e.g. big-O notation or stochastic gradient descent), but that's to be expected and sometimes it's useful to get a better foundation in the math behind it. Overall I found it useful but got tired of it by the last few chapters. It's also worth noting that it's already out of date - it was written in 2022 and has limited discussion of now-essential concepts like diffusion models. But that's not really the author's fault! You can learn about those online on your own.