A Next Big Idea Club Must-Read Title for July
Why Machines Learn, by the award-winning science writer Anil Ananthaswamy, takes the reader on an entertaining journey into the mind of a machine [The book] demystifies the underlying mechanisms behind machine learning, which may possibly lead to a better understanding of the learning process itself and the development of improved AI.
Physics World
A skillful primer makes sense of the mathematics beneath AI's hood.
New Scientist
Whether Ananthaswamy is talking of ML algorithms or manipulation of matrices, he maintains a lightness of language and invokes historical accounts to advance a compelling narrative A must-read for anyone who is curious to understand "the elegant math behind modern AI" [and] an inspirational guide for teachers of math and mathematical sciences who can adopt these techniques and methods to make classrooms lively.
Shaastra, IIT-Madras
Some books about the development of neural networks describe the underlying mathematics while others describe the social history. This book presents the mathematics in the context of the social history. It is a masterpiece. The author is very good at explaining the mathematics in a way that makes it available to people with only a rudimentary knowledge of the field, but he is also a very good writer who brings the social history to life.
Geoffrey Hinton, deep learning pioneer, Turing Award winner, former VP at Google, and Professor Emeritus at University of Toronto
After just a few minutes of reading Why Machines Learn, you ll feel your own synaptic weights getting updated. By the end you will have achieved your own version of deep learning with deep pleasure and insight along the way.
Steven Strogatz, New York Times bestselling author of Infinite Powers and professor of mathematics at Cornell University
If you were looking for a way to make sense of the AI revolution that is well underway, look no further. With this comprehensive yet engaging book, Anil Ananthaswamy puts it all into context, from the origin of the idea and its governing equations to its potential to transform medicine, quantum physics and virtually every aspect of our life. An essential read for understanding both the possibilities and limitations of artificial intelligence.
Sabine Hossenfelder, physicist and New York Times bestselling author of Existential Physics: A Scientist's Guide to Life's Biggest Questions
Why Machines Learn is a masterful work that explains in clear, accessible, and entertaining fashion the mathematics underlying modern machine learning, along with the colorful history of the field and its pioneering researchers. As AI has increasingly profound impacts in our world, this book will be an invaluable companion for anyone who wants a deep understanding of what s under the hood of these often inscrutable machines.
Melanie Mitchell, author of Artificial Intelligence and Professor at the Santa Fe Institute
Generative AI, with its foundations in machine learning, is as fundamental an advance as the creation of the microprocessor, the Internet, and the mobile phone. But almost no one, outside of a handful of specialists, understands how it works. Anil Ananthaswamy has removed the mystery by giving us a gentle, intuitive, and human-oriented introduction to the math that underpins this revolutionary development.
Peter E. Hart, AI pioneer, entrepreneur, and co-author of Pattern Classification
Anil Ananthaswamy s Why Machines Learn embarks on an exhilarating journey through the origins of contemporary machine learning. With a captivating narrative, the book delves into the lives of influential figures driving the AI revolution while simultaneously exploring the intricate mathematical formalism that underpins it. As Anil traces the roots and unravels the mysteries of modern AI, he gently introduces the underlying mathematics, rendering the complex subject matter accessible and exciting for readers of all backgrounds.
Björn Ommer, Professor at the Ludwig Maximilian University of Munich and leader of the original team behind Stable Diffusion
An inspiring introduction to the mathematics of AI.
Arthur I. Miller, author of The Artist in the Machine: The World of AI-Powered Creativity
[An] illuminating overview of how machine learning works.
Kirkus Reviews
