How Machines Learn

Machines are learning to drive cars, diagnose diseases, write essays, and create art. But how? What's actually happening inside these systems? The answer is mathematics—elegant, surprisingly accessible mathematics that anyone can understand. "How Machines Learn" reveals the complete story of artificial intelligence, from the simplest algorithms to the transformers powering today's breakthroughs. No PhD required. No prior coding experience needed. Just curiosity. You'll start where all machine learning begins: with functions, data, and the simple idea of learning from examples. Then, concept by concept, you'll build toward deep understanding: Discover how machines measure their mistakes and systematically improve. Master gradient descent—the optimization algorithm at the heart of everything. Build neural networks from individual neurons to multi-layer architectures. Understand backpropagation's clever use of calculus. Learn the practical tricks that make deep learning actually work. See how convolutional networks taught computers to see. Explore attention mechanisms and transformers—the revolution happening right now. Each chapter makes complex ideas clear. Mathematical concepts become intuitive. Abstract theories connect to real-world applications you use daily. By the final page, you'll understand not just what AI does, but why it works. You'll think differently about the intelligent systems reshaping our world. The beautiful math behind AI awaits. Let's make it simple.