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Part 1 of the review with chapters 1-6.
Grokking Artificial Intelligence Algorithms
Authors: Rishal Hurbans
Publisher: Manning Publications, 2020
Length: about 350 pages
An introductory guide to AI algorithms: search, evolutionary methods, swarm intelligence, ML, ANN, and Q-learning. Best read together with modern LLM/GenAI sources.
OriginalWhat this book is about
This is an accessible introduction to classical artificial intelligence algorithms: from search and evolutionary approaches to ML basics, ANN, and reinforcement learning.
The main limitation today: the book predates the widespread LLM/GenAI engineering wave, so it should be combined with more recent sources.
Structure: chapters 1-6
What is artificial intelligence
AI definition, short historical context from 1956, and the difference between narrow AI and AGI.
Search fundamentals
Core search algorithms: binary search, BFS, and DFS, plus intuition for algorithmic efficiency.
Smart search
Informed search (A*) and adversarial algorithms (min-max, alpha-beta pruning).
Evolutionary algorithms
Genetic algorithms: selection, mutation, crossover, and progressive population improvement.
Advanced evolutionary algorithms
Genetic and evolutionary programming, encoding strategies, and optimization scenarios.
Swarm intelligence: ants
Ant colony algorithms and pheromone trails as a strategy for route optimization.
Structure: chapters 7-10
Swarm intelligence: particles
Particle Swarm Optimization: particle movement through the search space using local and global experience.
Machine learning
Supervised, unsupervised, and reinforcement learning; regression, classification, and clustering tasks.
Artificial neural networks
ANN foundations: layers, forward pass, and training with backpropagation.
Reinforcement learning with Q-learning
Q-function, reward, action choice, and practical framing through MDP-style tasks.
Related chapter
Hands-On Large Language Models
The next step after a classical AI foundation.
How to read this book in 2026
The book covers fundamental AI algorithms well, but has little to no coverage of transformers, LLMs, and GenAI patterns.
Treat it as a first step: get strong basics in search/evolution/swarm/ML first, then move to modern LLM-oriented resources.
It may feel too basic for senior readers, but it is excellent for leveling shared terminology in a team.