Hurbans R. - Grokking AI Algorithms [2026, EPUB, ENG]

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Grokking AI Algorithms

Год издания: 2026
Автор: Hurbans R.

Издательство: Manning
ISBN: 978-1633434813
Язык: Английский

Формат: EPUB
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 592

Описание: 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

Книга в pdf-формате: https://rutracker.org/forum/viewtopic.php?t=6832255
[spoiler="Примеры страниц (скриншоты)"]

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[spoiler="Оглавление"]
preface
about this book
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
index
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