Why Machines Learn: The Elegant Math Behind Modern AI

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.

Outstanding introduction to the inner workings of AI. The author begins with simple, basic concepts and expands step by step into how AI functions. Though after a few chapters the math went beyond anything I ever studied, and I eventually glazed over the formulas, the lucid writing kept me understanding what the effect of the math was. The explanation not only followed a logical sequence, but a nearly chronological path as well, with discussions of many of the key pioneers and the impact of their contributions discussed in context. The author actually interviewed many of the key contributors, adding their words and thinking to the story. Though I will never come close to being able to work in the field, this book gave me the full and satisfying understanding of what AI is and how it works that I was seeking. If you read only one book about AI, this would be an excellent choice.

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 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 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.

Detta är en verkligt bra och pedagogisk bok som beskriver matematiken och historiken som ligger till grund för dagens AI. Läsaren kommer mycket långt bara genom att följa hur författaren guidar läsaren genom logiska matematiska resonemang som sedan kompletteras med den matematiska formeln för de som förstår den nivån.

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Un mattone, tutta la matematica dietro le ia.

The best introduction one might have to understand current dominant basis of AI (Artificial Neural Networks, Machine & Deep Learning). Require only high school math to follow the lead. For those who already understand the functioning of modern AI, the book contains a lot of historical fact mainly from people who shaped AI from Gauss to Hinton. Greatly Illustrated!

Very readable, despite the level of the maths - yes I know, there is a clue in the title! I have had an interest in machine learning and neural networks since 1989 so this was part refresher and part catch-up. It also filled in a lot of gaps in my understanding. Parts of it felt like heavy going but mostly it flowed really nicely with a good historical perspective. At the same time it is not afraid to present the technical (and mathematical) detail. Having said that, trying to read some of the formulae and diagrams on the Kindle edition was very tricky. I wouldn't recommend it for someone who is looking for an introduction to the subject, unless you have at least an A-level in maths! I'd hate people to be scared off the subject by jumping in at the deep end (of the learner pool). However, for someone who studied neural nets or expert systems in the 80s / 90s / 00s this is a really good way to get back into the subject and start to get your head around the amazing revolution we are experiencing in AI.