Grokking AI Algorithms, Second Edition

Understand the algorithms that underpin AI, from classic to cutting-edge. Artificial intelligence algorithms are the backbone of search and optimization, deep learning, reinforcement learning, and, of course, generative AI. Grokking AI Algorithms, Second Edition 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. 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 moder 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 You know you can solve a problem with AI--but how? Which algorithm do you pick and how do you properly implement it? Grokking AI Algorithms, Second Edition 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. About the book Grokking AI Algorithms, Second Edition teaches the theory behind AI with beautifully simple illustrations, step-by-step pseudocode, and intuitive explanations that make the math simple. You'll build a clear mental model of how different AI approaches fit together, then implement them with minimal code and maximum insight. Finally, you'll apply what you learn through engaging, end-to-end projects like finding your way through a maze with search algorithms, evolving knapsack solutions with genetic algorithms, teaching an agent to park itself with reinforcement learning, and exploring how LLMs and diffusion models power text and image generation. About the reader Readers need beginning to intermediate programming skills and high school level mathematics. No AI experience required. About the author Rishal Hurbans is a technologist, founde