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Why Machines Learn: The Elegant Math Behind Modern AI
by Anil Ananthaswamy
Published
July 16, 2024
Pages
480 pages
Language
English
Publisher
Dutton
Available Formats & Prices
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$16.99
Hardcover
$21.57
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About This Book
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 tumour is cancerous, or deciding whether someone gets bail. They now influence developments and discoveries in chemistry, biology, and physics—the study of genomes, extra-solar 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 artifical 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.
Introduction
In a world increasingly driven by artificial intelligence, understanding the foundational principles behind machine learning is more important than ever. This book delves into the elegant mathematics that powers modern AI, simplifying complex concepts and making them accessible to readers of all backgrounds. By unraveling the intricate theories that drive machine learning, the book offers a fresh perspective on how these algorithms shape our digital age and influence countless aspects of our daily lives.
Key Takeaways
Machine learning’s core lies in understanding elegant mathematical principles that guide data-driven decisions and innovations. Exploring AI's mathematical foundations reveals insights into the transformative role of algorithms in reshaping industries. This book bridges the gap between technical complexities and everyday applications of machine learning in society and technology.
Detailed Description
Why Machines Learn offers a captivating exploration of the mathematical foundations that underpin modern artificial intelligence and machine learning. The book explains crucial concepts like neural networks data patterns and optimization methods making complex theories accessible. Discover how these elements work together to drive machine learning’s dynamic evolution.
Delving into the history of AI this book traces its roots back to mathematical modeling and algorithmic advancements. Readers gain insight into how contemporary machine learning algorithms are shaped by earlier theoretical developments. This historical perspective enriches the understanding of AI’s current and future potential.
With an emphasis on clarity Why Machines Learn is designed for both seasoned tech enthusiasts and those new to AI. Readers will appreciate its clear explanations practical examples and intuitive diagrams that bring intricate mathematical concepts to life. The book further explores the ethical and societal implications of AI probing how machines learn to make decisions that impact the human experience.
This critical examination highlights the need for responsible AI development in today’s ever-evolving world. Ultimately Why Machines Learn invites readers on an enlightening journey that demystifies the elegant math fueling modern AI providing insights that inspire curiosity and deeper learning about the technologies shaping our future.
Standout Features
Why Machines Learn stands out by breaking down complex mathematical theories into intuitive concepts with clear explanations and relatable analogies This makes it accessible to both novices and experts The book uniquely intertwines AI's historical developments with present-day applications offering a comprehensive narrative that helps readers grasp the evolution of modern machine learning Its balanced exploration of ethical considerations and technical innovations sets the book apart emphasizing the impact of AI on society and encouraging thoughtful discourse on its future.
Book Details
0593185749
978-0593185742
6.38 x 1.5 x 9.3 inches
2.31 pounds
Specifications
Rating
Based on 45 ratings
Customer Reviews
A person focused history that caters to people with college level math
I had been working through other explanations of AI, but kept finding that I had forgotten too much of my college math to be able to fully understand the explanations. This book does an excellent job at providing explanation at the right times so I can understand each building block of the math behind AI. It is written both as a history, and as a set of mathematical building blocks so the reader gets a person focused understanding of the history of AI, and the reader gets to re-develop their math skills from the ground up to understand (abtracted) technical details of AI. I'd recommend this as the go-to book on AI for anyone who took multi-variable calculus in college (but forgot it in the intervening years).
Excellent introduction to the math and history behind machine learning and AI
I am doing a CalTech course on ML and AI, and I found this book to be a very good primer on the math behind the magic of packages such as TensorFlow, Pandas and Keras. Ananthaswamy provides a history of the development of the field intertwined with the math in a way that provides a grounded, contextual understanding of what is going on. While the concepts are sophisticated, at no point did I feel lost or uninterested. Anybody who has completed high school Precalculus should be able to handle most of the math in the book, with careful reading. Really cannot recommend this book highly enough - it really is excellent.
Very useful
Love it, thank you
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
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.
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.
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.
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!
