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1. Great historical review for both a general and technical audience
   This is essentially a brief history of machine learning. It’s not too technical but also touches on some interesting pieces of math that many newer people in the field might have missed. I particularly liked the details about some of the people who made discoveries along the way, including some that I didn’t know about; the biographical details are nice here. I’ve never been a fan of kernel methods and SVMs etc, but that section of the book was actually quite exciting and gave me more appreciation for this part of the field, for example.Highly recommended!

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2. Incredible narration of the history, people formulas & algorithms of ML
   I’m an engineer with experience in machine learning, so I purchased this book as a refresher on some of the milestones of our industry, thinking it would be a rundown of the major algorithms and proofs of how we got here. IT’S SO MUCH MORE THAN THAT. And, it’s incredible because of that.It’s a wonderfully-written narrative of the history of the people and their thought processes for developing the core ideas and then implementing them mathematically to bring about the practice of ML.It’s informative, entertaining, enriching, and worthwhile. No part of this book gets stale. It’s a real win.

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3. It is not clear who is supposed to be the reader of this book
   It is not clear who is supposed to be the reader of this book. It explains the mathematics, starting with essential calculus, and goes on to the formulas of deep learning. It is too tricky for readers unfamiliar with calculus and redundant to those familiar with it. I mainly enjoyed reading the last chapter about the current challenges of deep learning.

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4. Finally Clarity in my Generative AI confusion – a masterpiece!
   “Why Machines Learn”- is the book that finally clicked for me after two years of fumbling through Generative AI’s conceptual, software and mathematical maze (I seldom review books but this book merits it)I’ve been hooked on the wonders of conversational Generative AI, both its productivity and knowledge boosts using LLMs and Chatbots such as ChatGPT (granted watchful of probabilisitic hallucinations) , but the math? Not so much.My rusty dated college math tended to hold me back until this book cleared things up—historical context and all. Somewhat surprisingly to me I found the historical aspect greatly simplified and aided my mathematical understanding.I’ll admit, I had to utilize AI chatbot assistants for backup a few times and also I purchased the audio book as well to refresh my prior readings, but that’s part of the fun, right?If you’re curious about what makes machines learn and aren’t afraid to dust off some math, this book is your guide!

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5. History, Mathematics, Theory, and Philosophical aspects of ML, wrapped in compelling storytelling.
   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.

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6. A fantastic introduction to machine learning
   Great look into the story of machine learning and a good approach to it’s math. Must haves for coders and mathmatiscisans interested in the fantastical world of AI

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7. Very useful
   Love it, thank you

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8. Turned off in the first few pages
   It’s irritating that he started making just notation more complicated than it had to be in the first table example. But he turned something simple into something complex. This book should be the opposite.

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9. Anil’s book is a perfect introduction into machine learning (ML) maths for anyone who wants to start a journey reading more advanced ML books or papers with only high school maths. It starts with the basics and charts the history of how ML maths has evolved since the perceptron in 1943. Anil provides a lot of intuition behind the maths which is vital for a deeper understanding of the maths.Since I read Anil’s book I’ve started reading Kevin Murphy’s Probabilistic Machine Learning: An Introduction and I have to say it would have been impossible to get past chapter one without Anil’s excellent introduction.

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10. Though sometimes the simplification goes a step too far for my taste, the book does a good job to allow a look “under the hood” of Machine Learning. Like it, and it’s already ready by the next interested family member.

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11. This is a brilliant book

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12. I am reading this as a reader decades removed from University courses in multivariable calculus and linear algebra, but those faint memories are enough to get you through this fascinating book. It covers and reintroduces concepts in a very friendly and straightforward tone and the author is an excellent communicator of complex topics. If you’ve never been exposed to machine learning, you’ll be taken on a ride to explore perceptrons, k-nearest neighbours, PCA, and deep neural networks along with some equations and charts and lucidly written history that situates the motivation for these beautiful results. This book is a great way for people interested in going deeper into machine learning as a beginner as it will provide the background info that a good advisor may give. Recommended for the highly curious with technical aptitude.

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13. Anil Ananthaswamy’s book is a must-read for anyone intrigued by the world of machine learning and artificial intelligence. It offers a clear and accessible explanation of the fundamental mathematics—linear algebra and calculus—underpinning these technologies, tracing their historical roots and showing how they power today’s AI revolution. Whether you’re new to the subject or looking to deepen your understanding, this book provides valuable insights into how simple mathematical concepts are driving the advancements that shape our world today.

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