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Grokking Artificial Intelligence Algorithms
Rishal Hurbans
  • ISBN 9781617296185
  • 400 pages (estimated)
  • printed in black & white

Removes the fear of stepping into the mechanics of AI.

Kyle Peterson
Grokking Artificial Intelligence Algorithms is a fully-illustrated and interactive tutorial guide to the different approaches and algorithms that underpin AI. Written in simple language and with lots of visual references and hands-on examples, you’ll learn the concepts, terminology, and theory you need to effectively incorporate AI algorithms into your applications. And to make sure you truly grok as you go, you’ll use each algorithm in practice with creative coding exercises—including building a maze puzzle game, performing diamond data analysis, and even exploring drone material optimization.
Table of Contents detailed table of contents

0 Preface

1 Intuition of Artificial Intelligence

1.1 What is Artificial Intelligence?

1.1.1 A Definition of Artificial Intelligence

1.1.2 Understanding Data is Core to Understanding AI Algorithms

1.1.3 Algorithms are Instructions as Recipes

1.2 A Brief History of Artificial Intelligence

1.3 Problem Types and Problem-Solving Paradigms

1.3.1 Search Problems — Find a path to a solution

1.3.2 Optimization Problems — Find a good solution

1.3.3 Prediction and Categorization Problems — Learn from patterns in data

1.3.4 Clustering Problems — Identify patterns in data

1.3.5 Deterministic Models

1.3.6 Stochastic / Probabilistic Models

1.4 Intuition of Artificial Intelligence Concepts

1.4.1 Narrow Intelligence — Specific purpose solutions

1.4.2 General Intelligence — Human like solutions

1.4.3 Super Intelligence — The great unknown

1.4.4 Old AI and New AI

1.4.5 Search Algorithms

1.4.6 Biology Inspired Algorithms

1.4.7 Machine Learning Algorithms

1.4.8 Reinforcement Learning Algorithms

1.4.9 Deep Learning Algorithms

1.5 Uses for Artificial Intelligence Algorithms

1.5.1 Agriculture — Optimal Plant Growth

1.5.2 Banking — Fraud Detection

1.5.3 Cyber Security — Attack Detection and Handling

1.5.4 Healthcare — Diagnosis of Patients

1.5.5 Logistics — Routing and Optimization

1.5.6 Telecoms — Optimizing Networks

1.5.7 Games — Creating AI Agents

2 Search Fundamentals

2.1 What is Planning and Searching?

2.2 Cost of Computation — The Reason for Smart Algorithms

2.3 Problems Applicable to Searching Algorithms

2.4 Representing State — Creating a framework to represent problem spaces and solutions

2.4.1 Graphs — Representing search problems and solutions

2.4.2 Trees — The concrete structure used to represent search solutions

2.5 Uninformed Search — Blindly looking for solutions

2.5.1 Breadth-first Search — Look wide before looking deep

2.5.2 Depth-first Search — Look deep before looking wide

2.5.3 Use Cases for Uninformed Search Algorithms

2.5.4 Optional: More About Graph Categories

2.5.5 Optional: More Ways to Represent Graphs

3 Intelligent Search

3.1 Defining Heuristics — Designing educated guesses

3.2 Informed Search — Looking for solutions with guidance

3.2.2 Use Cases for Informed Search Algorithms

3.3 Adversarial Search — Looking for solutions in a changing environment

3.3.1 A Simple Adversarial Problem

3.3.2 Min-max Search — Simulate actions and choose the best future

3.3.3 Alpha-beta Pruning — Optimize by exploring the sensible paths only

3.3.4 Use Cases for Adversarial Search Algorithms

4 Evolutionary Algorithms

4.1 What is Evolution?

4.2 Problems Applicable to Evolutionary Algorithms

4.3 Genetic Algorithm – Life Cycle

4.4 Encoding the Solution Space

4.4.1 Binary Encoding – Represent possible solutions with zeros and ones

4.5 Creation of a Population of Solutions

4.6 Measuring Fitness of Individuals in a Population

4.7 Selecting Parents Based on their Fitness

4.7.1 Steady State – Replacing a portion of the population each generation

4.7.2 Generational – Replacing the entire population each generation

4.7.3 Roulette Wheel Selection – Selecting parents and surviving individuals

4.8 Reproducing Individuals from Parents

4.8.1 Single-point Crossover – Inherit one part from each parent

4.8.2 Two-point Crossover – Inherit more parts from each parent

4.8.3 Uniform Crossover – Inherit many parts from each parent

4.8.4 Bit String Mutation for Binary Encoding

4.8.5 Flip Bit Mutation for Binary Encoding

4.9 Populating the Next Generation

4.9.1 Exploration vs. Exploitation

4.9.2 Stopping Conditions

4.10 Configuring the Parameters of a Genetic Algorithm

4.10.1 Use Cases for Evolutionary Algorithms

5 Advanced Evolutionary Approaches

5.1 Evolutionary Algorithm Lifecycle Recap

5.2 Alternative Selection Strategies

5.2.1 Rank Selection – Even the playing field

5.2.2 Tournament Selection – Let them fight

5.2.3 Elitism Selection – Only the best

5.3 Real-value Encoding – Working with real numbers

5.3.1 Real-value Encoding

5.3.2 Arithmetic Crossover – Reproduce with math

5.3.3 Boundary Mutation – Real-value Encoding

5.3.4 Arithmetic Mutation – Real-value Encoding

5.4 Order Encoding – Working with sequences

5.4.1 Importance of the Fitness Function

5.4.2 Order Encoding

5.4.3 Order Mutation – Order/Permutation Encoding

5.5 Tree Encoding – Working with hierarchies

5.5.1 Tree Encoding At its Core

5.5.2 Tree Crossover – Inheriting portions of a tree

5.5.3 Change Node Mutation – Changing the value of a node

5.6 Common Types of Evolutionary Algorithms

5.6.1 Genetic Programming

5.6.2 Evolutionary Programming

5.7 Glossary of Evolutionary Algorithm Terms

5.8 More Use Cases for Evolutionary Algorithms

6 Swarm Intelligence: Ants

6.1 What is Swarm Intelligence?

6.2 Ant Colony Optimization

6.2.1 Problems Applicable to Ant Colony Optimization

6.2.2 Representing State

6.2.3 Ant Colony Optimization Lifecycle

6.2.4 Initialize the Pheromone Trails

6.2.5 Setup the Population of Ants

6.2.6 Choose the Next Visit

6.2.7 Update the Pheromone Trails

6.2.8 Update the Best Solution

6.2.9 Determine the Stopping Criteria

6.2.10 Use Cases for Ant Colony Optimization Algorithms

7 Swarm Intelligence: Particles

8 Machine Learning

9 Artificial Neural Networks

10 Reinforcement Learning

About the Technology

AI is primed to revolutionize the way we build applications, offering exciting new ways to solve problems, uncover insights, innovate new products, and provide better user experiences. Successful AI is based on a set of core algorithms that form a base of knowledge shared by all data scientists. Grokking Artificial Intelligence Algorithms opens the lid on AI’s black box, shining a light on how AI algorithms work and how to use them effectively.

About the book

Grokking Artificial Intelligence Algorithms uses simple language, jargon-busting explanations, and hand-drawn diagrams to open up complex algorithms. Don’t worry if you aren’t a calculus wunderkind; you’ll need only the algebra you picked up in math class. Creative coding exercises take you hands-on with the core and most-common algorithms, as you learn to classify data with a neural network, navigate a maze with a functional decision-making algorithm, create an autonomous agent to play two-player games, and more. As you start to grok how AI works under the hood, exciting and innovative ways to apply AI to your software will click into place. When you’re done, you’ll be able to identify problems best solved by AI and be able to select the perfect algorithms to solve them!

What's inside

  • Use cases for different AI algorithms
  • How to encode problems and solutions using data structures
  • Intelligent search for game playing
  • Ant colony algorithms for path finding
  • Evolutionary algorithms for optimization problems
  • Reinforcement learning algorithms for decision making
  • How to prepare data, and the building blocks of machine learning
  • Artificial neural networks for learning patterns and making decisions

About the reader

For software developers with high school-level algebra and calculus skills.

About the author

Rishal Hurbans is a solutions architect for one of the largest technology companies in South Africa, the founder of the Artificial Intelligence South Africa group, and an Intel Innovator in the AI space.

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