How AI Algorithms Think and Learn

Описание: This video walks through the opening chapter of Grokking AI Algorithms, Second Edition by Rushal Hurbans. It sets the stage for the whole book by answering a practical question many developers and data scientists run into early: what kinds of problems does AI actually solve, and how do algorithms fit into that picture?

You’ll get a clear, visual introduction to the ideas that power modern AI—search, learning, planning, and adaptation—without assuming a math-heavy background. The focus is on intuition: how different algorithms behave, why they exist, and when one approach makes more sense than another. This chapter also previews how today’s systems, including large language models and image generators, connect back to these core ideas.

If you’re considering the book, this summary is a good way to see the teaching style and level of depth. If you’re already reading it, think of this as a refresher that helps lock in the big picture before diving into code and exercises.

What you’ll learn from this chapter:
The main categories of AI problems and how algorithms map to them
Why search sits at the foundation of many intelligent systems
How learning-based approaches differ from rule-based ones
How modern AI builds on classic ideas rather than replacing them

Grokking AI Algorithms, Second Edition uses simple language, step-by-step pseudocode, and illustrations to explain everything from genetic algorithms and neural networks to reinforcement learning, large language models, and diffusion-based image generation. It’s written for people who want to understand how AI works under the hood, not just call a library and hope for the best.

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Learn more about the book at Manning Publications, and subscribe for more author talks, chapter summaries, and practical deep dives into software and AI topics.

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