Why Machines Learn

Why Machines Learn: The Elegant Math Behind Modern AI, authored by Anil Ananthaswamy. These snippets include the title page, copyright information, and several glowing endorsements from notable figures like Steven Strogatz and Sabine Hossenfelder, establishing the book as a comprehensive and engaging guide to the AI revolution. The main body of the excerpts explores the mathematical and historical underpinnings of machine learning (ML), ranging from basic concepts like vectors, the dot product, and probability theory, including Bayes's theorem, to more complex ML algorithms. Key topics addressed include the least mean squares (LMS) algorithm, the universal approximation theorem, the development of convolutional neural networks (CNNs) like LeNet and AlexNet, and the inner workings of large language models (LLMs), discussing phenomena like overfitting and "grokking." The material serves to clarify the complex mechanics of modern artificial intelligence through accessible explanations, historical context, and mathematical examples.