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Why Machines Learn: The Elegant Math Behind Modern AI

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A rich, narrative explanation of the mathematics that has brought us machine learning and the ongoing explosion of artificial intelligence
Machine learning systems are making life-altering decisions for us: approving mortgage loans, determining whether a tumor is cancerous, or deciding if someone gets bail. They now influence developments and discoveries in chemistry, biology, and physics—the study of genomes, extrasolar planets, even the intricacies of quantum systems. And all this before large language models such as ChatGPT came on the scene.
We are living through a revolution in machine learning-powered AI that shows no signs of slowing down. This technology is based on relatively simple mathematical ideas, some of which go back centuries, including linear algebra and calculus, the stuff of seventeenth- and eighteenth-century mathematics. It took the birth and advancement of computer science and the kindling of 1990s computer chips designed for video games to ignite the explosion of AI that we see today. In this enlightening book, Anil Ananthaswamy explains the fundamental math behind machine learning, while suggesting intriguing links between artificial and natural intelligence. Might the same math underpin them both?
As Ananthaswamy resonantly concludes, to make safe and effective use of artificial intelligence, we need to understand its profound capabilities and limitations, the clues to which lie in the math that makes machine learning possible.
In a brand-new afterword exclusively in the paperback edition, Ananthaswamy dives into the Transformer architecture that makes large language models like ChatGPT possible and points to groundbreaking future directions enabled by the technology.
Publisher ‏ : ‎ Dutton
Publication date ‏ : ‎ August 26, 2025
Edition ‏ : ‎ Reprint
Language ‏ : ‎ English
Print length ‏ : ‎ 496 pages
ISBN-10 ‏ : ‎ 0593185765
ISBN-13 ‏ : ‎ 978-0593185766
Item Weight ‏ : ‎ 1.04 pounds
Dimensions ‏ : ‎ 6.04 x 1.04 x 8.95 inches
Best Sellers Rank: #18,764 in Books (See Top 100 in Books) #1 in Computer Science (Books) #1 in Computer Vision & Pattern Recognition #1 in Discrete Mathematics (Books)
Customer Reviews: 4.6 4.6 out of 5 stars 527 ratings var dpAcrHasRegisteredArcLinkClickAction; P.when(‘A’, ‘ready’).execute(function(A) { if (dpAcrHasRegisteredArcLinkClickAction !== true) { dpAcrHasRegisteredArcLinkClickAction = true; A.declarative( ‘acrLink-click-metrics’, ‘click’, { “allowLinkDefault”: true }, function (event) { if (window.ue) { ue.count(“acrLinkClickCount”, (ue.count(“acrLinkClickCount”) || 0) + 1); } } ); } }); P.when(‘A’, ‘cf’).execute(function(A) { A.declarative(‘acrStarsLink-click-metrics’, ‘click’, { “allowLinkDefault” : true }, function(event){ if(window.ue) { ue.count(“acrStarsLinkWithPopoverClickCount”, (ue.count(“acrStarsLinkWithPopoverClickCount”) || 0) + 1); } }); });

Customers say

Customers find the book provides an excellent introduction to machine learning math, making complex ideas surprisingly accessible. The content is well-structured and easy to read, with one customer noting how the underlying mathematics is carefully woven together with history. They appreciate the historical content, with one review highlighting the excellent exposition of neural network evolution, and find the book engaging and worth the investment.

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