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

I found this book illuminating and clear. You have to work through the material in order to understand the historical and technical development of AI. The equations are sometimes a bit confusing because algebraic numerals sometimes seem like ordinary numbers. However, this aside, the book provides a clear account of the mathematical basis of AI and how some of the key figures made their discoveries. An excellent book.

Suitable for undergraduate (CS and engineering) use. Well written and explained.

Really enjoyed this very readable book. I used to work in the field, back in the later 80s, and it was fun to go over old ground and see how ML has since progressed. I also enjoyed the maths, reacquainting myself with the techniques and concepts, some of which I had become a little rusty on.The style is excellent, mixing historical anecodotes with very clear explantions of the principles and workings of the main ML architectures, with numerous easy to follow examples and frequent useful reminders of concepts covered elsewhere in the book. The book title is spot on as a description of its contents.If I have a minor criticism, its that the equations could have been more readable, at least in the kindle edition I read. Fonts used in the text sometimes did not match those in the equations, and the kindle rendering of equations was quite variable (I was using Android kindle on a Galaxy tablet), virtually unreadably so on a Kindle Paperwhite. I'm guessing that is an issue with the kindle readers rather than Anil's writings.

Nicely balances the history of deep learning with an introduction to the maths. Anyone with a good GCSE in Maths should be ok with the level of the book, it touches on lineal algebra, calculus and probability, but it’s just the gist and doesn’t dwell on detailed proofs. Notation is simplified (I find the full notation one of the biggest barriers) and important concepts like the function of a perceptron are repeated several times throughout the book, in case you skimmed them.

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.

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.

Simply brilliant

Even if nothing else is shown, this is possibly the most useful single sample page in the book for a prospective purchaser rather than some random excerpt from the general text.