Why Machines Learn: The Elegant Math Behind Modern AI Hardcover – July 16, 2024

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.

One of the best books you will find on AI and ML anywhere. I wanted to thank the author. Well written to cover the history and math behind AI. Beginners can skip the detailed math and proofs. I just hope a next edition gets more into attention networks and transformers.

Given the increasing use of machine learning embedded within everyday software as well as its greater use in aiding decision making, an overview of the foundation for non-experts is a useful addition. The book goes through both the history as well as many of the main algorithmic ideas in a straightforward way that allows one to follow along irrespective of mathematical background. The criticism I have is merely that it starts out by assuming 0 knowledge to frame some basic mathematical notation and ideas and then eventually gets into topics which require some linear algebra and calculus to appreciate. This isn't in itself a bad thing but it ends up being an internal inconsistency of level of math in the book as it is highly unlikely a reader would be able to follow the details of the second half from having learnt the math from the first half.The book is split into 12 chapters going from basic math to neural networks. It discusses what the uses of machine learning are and its basic statistical nature of finding patterns in data through the use of computers. The field has a rich history crossing computer science, information theory and mathematical statistics. Starting out by going through the computer science and math the author and the ideas of feature space and linear algebra including PCA and eigenvectors. He then moves on to some early days when algorithms were being developed and discusses how the SVM algorithm was developed and his source interviews include Thomas Cover, the author of the main information theory textbook. He discusses Hopfield networks and how networks can store memory and then moves on to deep neural networks and the early work of Yan Le Cun and Geoffrey Hinton. This is where the book for me was most interesting as he discusses the puzzling nature of double descent and grokking in the training of large neural networks and some experts perspectives on these topics.Overall the book is readable but for me was slow to get started and then much more interesting in the latter half. I don't think one can learn the math for the second half from the first half as mentioned above and for that reason I found it a bit inconsistent in slow but the overall material was enjoyable to read think the book is a good effort on giving an overview of a field in the popular imagination.

Anil's storytelling added human faces to many names I was already familiar with, but only in an abstract way. That's the history part, written in a very personal and engaging way that only a good writer can do. At the same time the history of the development of ML theory is complete and expounded upon in enough detail that anyone with college level math abilities could follow along if so desired. (I expect many will skip some of those parts either because they know it or they don't need to know it. Perhaps those sections could be better sectioned to enable skipping.) Finally he asks very good questions about the nature of intelligence and how AI does or does not overlap with human intelligence, and well as the dangers it poses and benefits it may offer.The way the author maintains the big picture while leading the reader through a "live" minute-by-minute narration of compelling details reminds me of the style of VS Naipal, despite being a completely different genre.

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.

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.