Why Machines Learn: The Elegant Math Behind Modern AI

Anil Ananthaswamy has done something truly special with Why Machines Learn. In a field often dominated by jargon and overwhelming technicality, he offers a remarkably elegant and readable exploration of the mathematical principles that underpin modern artificial intelligence. This book doesn’t just explain what machine learning is — it illuminates why it works, and it does so with clarity, depth, and a journalist’s gift for storytelling.What sets this book apart is its rare ability to blend rigorous concepts with intuitive explanations. Ananthaswamy takes readers through linear algebra, probability theory, optimization, and other foundational tools, not in isolation, but as they come alive within real-world AI applications. Whether he’s explaining how gradient descent mimics nature or demystifying neural networks, he makes complex ideas feel surprisingly accessible.This is not a textbook, and it’s not just for data scientists — it’s for anyone curious about the logic that powers today’s intelligent systems. If you’ve ever wanted to understand the beauty behind the algorithms shaping our world, this book is a must-read.Highly recommended for tech enthusiasts, students, and lifelong learners alike.

This is an excellent book! It is insightful and revealing. I had read three other books on AI which focus on coding and that reference technical papers for the algorithms involved. However this book presents the math is a really intuitive form. It provides a perspective that is open to uncertainty and that provide a platform to include your interpretation while delving in the history that involve the intellectual giants that created all the bases for a now common used tools that are known superficially by a grand portions of users. This book is highly recommended!!

I’m reluctant to give this anything other than five stars because I can only imagine how much work it took to write. Ananthaswamy clearly put a lot of effort into what is a “light” math textbook.You should read this if you’re curious about the math behind machine learning. It’s not complicated, and the journey is worth it.Here’s what I think could be better; perhaps for the paperback/revised edition:- I’m puzzled by the choice of math notation. It’s much more natural to have subscripts for x1, x2, etc., but the author chose to inform of us of this fact, and then didn’t use subscripts. This makes the math more complicated to read, not less.- Similarly, ŷ (y hat) is the symbol for the estimate of y; the author informs us of this and then writes “yhat” throughout, which is puzzling.- A bit too woke for me overall (“no person’s land” rather than the usual “no man’s land” which I don’t think any reasonable person would have a problem with)The biggest problem, though, is that we get all of two pages, in a book that’s over 400 pages long, on transformers. We get very little on ChatGPT. And get end on a depressing epilogue.The book should have a lot more detail on the juiciest and most interesting parts of machine learning: what’s happening today with transformers and ChatGPT. And it should end on a positive note, informing the reader where the research is headed and what the possibilities are.

This is the best science book I have read in two decades. I have a mathematics background (MSc in Electrical Engineering and a doctorate heavy on structural equation modeling), which helps wehn reading the book.However, a modest knowledge of linear algebra and calculus will suffice. ML and LLM are not that complicated when taking a helicopter view of the AI field. The scale of what is being done, at speed, is what impresses me.The books is succinctly written. It is possible to skip the details in the matrix manipulations and only follow the main arguments.Overall, the best introduction to AI I know of.

This is definitely a great book for anyone who wants to start digging deep into the world of AI.It’s extremely pleasant to read, especially because it explores the history of AI development and combines it with just the right amount of mathematical depth for non-experts—or at least for those like me who have been a bit distant from mathematics in recent years.It’s the perfect starting point to understand AI, neural networks, machine learning, deep learning, and the mathematical foundations that made them possible.

Excellent exposition of the evolution of neural networks and machine learning along with its underlying math. Math majors, software engineers and physicists will find the math quite accessible. Though the math becomes progressively more difficult it is explained in such a manner that even the non-mathematician will walk away with an understanding of how the underlying algorithms drive machine learning.

Truly an exceptional book. The author explains the math behind artificial intelligence to anyone who is interested. Whenever he introduces a mathematical concept, the author first explains the concept in general and then explains its applicability to artificial intelligence. This format is not limited to higher math; the author clearly explains virtually any mathematical concept beyond arithmetic (e.g., the author even explains vectors). Bottom line, the book is highly informative and readily accessible to anyone interested in artificial intelligence, regardless of their background.