Grokking Artificial Intelligence Algorithms

A visual, intuitive introduction to core AI algorithms for developers and curious minds

Understand how AI algorithms actually work — from search and optimization to neural networks — so you can apply them with confidence.

R Rishal Hurbans

Grokking Artificial Intelligence Algorithms breaks down the core techniques behind modern AI using clear diagrams, worked examples, and plain language. Rishal Hurbans walks you through search, optimization, evolutionary algorithms, swarm intelligence, and neural networks without burying you in theory. Each concept is grounded in practical scenarios that make the mechanics visible. If you want to genuinely understand AI rather than just use it as a black box, this is your starting point.

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About this book

Most introductions to AI either drown you in mathematics or skip the fundamentals entirely in favour of library calls. Grokking Artificial Intelligence Algorithms takes a different path. Rishal Hurbans explains the ideas that power AI systems — how they search, learn, optimise, and adapt — using consistent visual representations and concrete examples that make abstract concepts stick.

The book covers a broad map of AI: uninformed and informed search, heuristics, evolutionary algorithms, genetic algorithms, swarm intelligence, neural networks, and reinforcement learning. You work through each family of algorithms at the conceptual level first, then see how the pieces fit together. By the time you finish, terms like "fitness function", "backpropagation", or "pheromone trail" are tools you understand, not jargon you look up.

The approach is deliberately visual. Nearly every algorithm is illustrated with step-by-step diagrams showing state changes, decision points, and data flow. This matters: AI algorithms often resist understanding when presented as equations alone. Seeing the algorithm move through a problem is what turns reading into comprehension.

Practical Python-style pseudocode and real-world problem framings appear throughout, bridging the gap between concept and implementation. You finish each chapter able to describe what a given algorithm does, why you would choose it, and what its trade-offs are against alternatives.

Search algorithms: breadth-first, depth-first, A*, and beyond
Heuristic and evolutionary approaches to hard optimisation problems
Swarm intelligence: ant colony optimisation and particle swarms
Neural networks: structure, training, and backpropagation
Reinforcement learning fundamentals and agent design

This book will not teach you to tune a production TensorFlow model. What it will do is give you the conceptual foundation that makes every tutorial, paper, or framework you touch afterward make sense. That foundation is worth building before anything else.

🎯 What you'll learn

Trace how uninformed and informed search algorithms explore problem spaces to find solutions
Apply heuristic thinking to guide algorithms toward good solutions under real constraints
Explain how genetic algorithms encode, evolve, and select solutions to optimisation problems
Describe swarm intelligence techniques such as ant colony optimisation and particle swarm optimisation
Map the structure of a neural network and follow how backpropagation adjusts weights during training
Distinguish reinforcement learning from supervised learning and understand how agents learn from feedback
Choose between algorithm families based on problem type, constraints, and trade-offs

👤 Who is this book for?

Developers who use AI libraries but want to understand what is actually happening under the hood
Computer science students seeking a readable companion to drier textbook treatments of AI
Software engineers preparing for roles that involve machine learning and wanting a solid conceptual baseline
Analysts or technical product managers who need to reason about AI system design without becoming researchers
Self-taught programmers taking their first serious step into artificial intelligence

01

Intuitions of Artificial Intelligence

Establishes a working vocabulary for AI and frames the landscape of algorithm families covered in the book. You build mental models for how problems are represented before any algorithm is introduced.
02

Search Fundamentals

Covers uninformed search strategies including breadth-first and depth-first search. You trace their execution on graph problems and compare how they explore the solution space.
03

Informed Search

Introduces heuristic search and the A* algorithm. You learn how heuristic functions guide search toward goals faster and see where they can mislead if poorly designed.
04

Evolutionary Algorithms

Explains the biological metaphors behind evolutionary computation and introduces the general evolutionary algorithm loop. You see how populations of candidate solutions are evaluated and selected.
05

Genetic Algorithms

Builds on evolutionary concepts to show how genetic algorithms encode solutions, apply crossover, and introduce mutation. You work through a concrete optimisation problem from encoding to result.
06

Swarm Intelligence

Covers ant colony optimisation and particle swarm optimisation. You trace how distributed, agent-based behaviour produces globally intelligent solutions to routing and optimisation problems.
07

Neural Networks

Constructs a neural network from the neuron level up, explaining weighted inputs, activation functions, and the forward pass. You follow a training cycle and see how errors propagate backward through the network.
08

Reinforcement Learning

Introduces agents, environments, rewards, and policies. You trace a Q-learning example and understand how an agent builds knowledge through repeated interaction rather than labelled data.
09

Algorithm Selection and Real-World Application

Synthesises the book by comparing algorithm families across problem types, constraints, and cost profiles. You leave with a decision framework for choosing the right approach before writing a line of code.

Frequently asked questions

Do I need a strong mathematics background to read this book?

No. The book deliberately minimises formal mathematics and prioritises visual and conceptual explanations. Basic algebra is sufficient; calculus is not required.

Does the book include runnable code?

The book uses Python-style pseudocode throughout to illustrate algorithms. It is oriented toward conceptual understanding rather than being a hands-on coding tutorial, so full project code is not the primary deliverable.

Is this book suitable for complete beginners to programming?

You should be comfortable reading simple code and understand basic data structures such as lists and trees. Prior programming experience helps significantly, though deep expertise is not required.

How does this compare to a formal AI textbook like Russell and Norvig?

It covers less breadth and depth than a university textbook, but it is far more readable and visual. It works well as a first pass before or alongside a more rigorous text.

Was this book written for a specific programming language?

No. The concepts and pseudocode are language-agnostic, so the material applies regardless of whether you work in Python, Java, or another language.

Is the content still relevant given how fast AI has moved since 2020?

The foundational algorithms the book covers — search, evolutionary methods, neural networks, reinforcement learning — are stable and have not been superseded. The conceptual grounding remains as valid today as at publication.