Grokking Artificial Intelligence Algorithms

”This book takes an impossibly broad area of computer science and communicates what working developers need to understand in a clear and thorough way.” - David Jacobs, Product Advance Local
Key Features
Master the core algorithms of deep learning and AI
Build an intuitive understanding of AI problems and solutions
Written in simple language, with lots of illustrations and hands-on examples
Creative coding exercises, including building a maze puzzle game and exploring drone optimization
About The Book

“Artificial intelligence” requires teaching a computer how to approach different types of problems in a systematic way. The core of AI is the algorithms that the system uses to do things like identifying objects in an image, interpreting the meaning of text, or looking for patterns in data to spot fraud and other anomalies. Mastering the core algorithms for search, image recognition, and other common tasks is essential to building good AI applications

Grokking Artificial Intelligence Algorithms uses illustrations, exercises, and jargon-free explanations to teach fundamental AI concepts.You’ll explore coding challenges like detect­ing bank fraud, creating artistic masterpieces, and setting a self-driving car in motion. All you need is the algebra you remember from high school math class and beginning programming skills.

What You Will Learn

Use cases for different AI algorithms
Intelligent search for decision making
Biologically inspired algorithms
Machine learning and neural networks
Reinforcement learning to build a better robot

This Book Is Written For
For software developers with high school–level math skills.

About the Author
Rishal Hurbans is a technologist, startup and AI group founder, and international speaker.

Table of Contents

1 Intuition of artificial intelligence
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 with Q-learning