Artificial intelligence algorithms are the backbone of search and optimization, deep learning, reinforcement learning, and, of course, generative AI. This book introduces the most important AI algorithms using relatable illustrations, interesting examples, and thought-provoking exercises. Written in simple language and with lots of visual references and hands-on code examples, it helps you build a natural intuition into how intelligent systems learn, plan, and adapt. This second edition has been thoroughly revised, with new chapters on large language models, image generation, and more. You know you can solve a problem with AI—but how? Which algorithm do you pick and how do you properly implement it? This book makes it simple and easy to understand the most core and common AI approaches. You’ll learn how to understand problem types, map real-world tasks to those problems, and how to design and implement the right algorithm—all following clear visual examples, pseudocode, and learning-oriented examples. In Grokking AI Algorithms, Second Edition you will discover: • How to pick the right algorithm for each AI problem • Learn the fundamentals of search (the foundation of modern AI) • Building intelligent agents to solve puzzles • Finding solutions using the theory of evolution and genetic algorithms • Make predictions with neural networks • Understand how AI gets better with reinforcement learning • Building a LLM pipeline and image diffusion model from scratch About the technology AI algorithms are the backbone of search and optimization problems, deep learning, reinforcement learning, and, of course, generative AI. But knowing which algorithm to use—and why—is often harder than writing the code itself. Grokking AI Algorithms, Second Edition illuminates the algorithms behind modern generative AI with clear explanations, step-by-step code examples, and beautifully simple illustrations. About the book Everything you’ll learn in this powerfully simple book is reinforced through engaging, end-to-end projects—from solving mazes with search algorithms to navigating a car through a crowded parking lot with reinforcement learning. Plus, this second edition has been thoroughly revised with fresh chapters exploring the core algorithms of LLMs and image generation models. What's inside • Search algorithms and swarm optimization • Deep learning and neural networks • Training AI with reinforcement learning • Building a LLM pipeline for text generation About the reader Requires beginning to intermediate programming skills and high school level mathematics. No AI experience required. About the author Rishal Hurbans is an experienced technologist and serial entrepreneur specializing in AI engineering and human performance. Table of Contents 1 Intuition of AI 2 Search fundamentals 3 Intelligent search 4 Evolutionary algorithms 5 Advanced evolutionary approaches 6 Swarm intelligence: Ants 7 Swarm intelligence: Particles 8 Machine learning 9 Artificial neural networks 10 Reinforcement learning 11 Large language models 12 Generative image models