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

by Hala Nelson

Essential Math for Next-Level Mathematics for Efficient and Successful AI Systems is purchased directly from the publisher or approved distributor and spiraled by a 3rd party. Seller is not affiliated with, endorsed by, or pre-authorized by the publisher or author for the spiral listing. Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you're just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field. Understand the underlying mathematics powering AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, optimal control, and more Learn how to adapt mathematical methods to different applications from completely different fields Gain the mathematical fluency to interpret and explain how AI systems arrive at their decisions

Genres: Mathematics, Artificial Intelligence, Technology, Computer Science

602 pages, Spiral-bound

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.