Grokking Artificial Intelligence Algorithms
"From start to finish, the best book to help you learn AI algorithms and recall why and how you use them." - Linda Ristevski, York Region District School Board

”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
1137832073
Grokking Artificial Intelligence Algorithms
"From start to finish, the best book to help you learn AI algorithms and recall why and how you use them." - Linda Ristevski, York Region District School Board

”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
43.99 In Stock
Grokking Artificial Intelligence Algorithms

Grokking Artificial Intelligence Algorithms

by Rishal Hurbans
Grokking Artificial Intelligence Algorithms

Grokking Artificial Intelligence Algorithms

by Rishal Hurbans

eBook

$43.99 

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Overview

"From start to finish, the best book to help you learn AI algorithms and recall why and how you use them." - Linda Ristevski, York Region District School Board

”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

Product Details

ISBN-13: 9781638355687
Publisher: Manning
Publication date: 07/20/2020
Sold by: SIMON & SCHUSTER
Format: eBook
Pages: 392
File size: 24 MB
Note: This product may take a few minutes to download.

About the Author

Rishal Hurbans is a technologist, founder, and international speaker.

Table of Contents

Preface ix

Acknowledgments xvii

About this book xix

About the author xxiii

1 Intuition of artificial intelligence 1

What is artificial intelligence? 1

A brief history of artificial intelligence 6

Problem types and problem-solving paradigms 8

Intuition of artificial intelligence concepts 10

Uses for artificial intelligence algorithms 14

2 Search fundamentals 21

What are planning and searching? 21

Cost of computation: The reason for smart algorithms 24

Problems applicable to searching algorithms 25

Representing state: Creating a framework to represent problem spaces and solutions 28

Uninformed search: Looking blindly for solutions 34

Breadth-first search: Looking wide before looking deep 36

Depth-first search: Looking deep before looking wide 45

Use cases for uninformed search algorithms 53

Optional: More about graph categories 54

Optional: More ways to represent graphs 56

3 Intelligent search 59

Defining heuristics: Designing educated guesses 59

Informed search: Looking for solutions with guidance 63

Adversarial search: Looking for solutions in a changing environment 72

4 Evolutionary algorithms 91

What is evolution? 91

Problems applicable to evolutionary algorithms 95

Genetic algorithm: Life cycle 99

Encoding the solution spaces 102

Creating a population of solutions 108

Measuring fitness of individuals in a population 110

Selecting parents based on their fitness 112

Reproducing individuals from parents 116

Populating the next generation 122

Configuring the parameters of a genetic algorithm 126

Use cases for evolutionary algorithms 127

5 Advanced evolutionary approaches 131

Evolutionary algorithm life cycle 131

Alternative selection strategies 133

Real-value encoding: Working with real numbers 137

Order encoding: Working with sequences 141

Tree encoding: Working with hierarchies 144

Common types of evolutionary algorithms 148

Glossary of evolutionary algorithm terms 149

More use cases for evolutionary algorithms 150

6 Swarm intelligence: Ants 153

What is swarm intelligence? 153

Problems applicable to ant colony optimization 156

Representing state: What do paths and ants look like? 160

The ant colony optimization algorithm life cycle 164

Use cases for ant colony optimization algorithms 187

7 Swarm intelligence: Particles 189

What is particle swarm optimization? 189

Optimization problems: A slightly more technical perspective 192

Problems applicable to particle swarm optimization 195

Representing state: What do particles look like? 198

Particle swarm optimization life cycle 199

Use cases for particle swarm optimization algorithms 223

8 Machine learning 227

What is machine learning? 227

Problems applicable to machine learning 230

A machine learning workflow 232

Classification with decision trees 256

Other popular machine learning algorithms 275

Use cases for machine learning algorithms 276

9 Artificial neural networks 279

What are artificial neural networks? 280

The Perceptron: A representation of a neuron 283

Defining artificial neural networks 287

Forward propagation: Using a trained ANN 295

Backpropagation: Training an ANN 303

Options for activation functions 314

Designing artificial neural networks 316

Artificial neural network types and use cases 319

10 Reinforcement learning with Q-learning 323

What is reinforcement learning? 323

Problems applicable to reinforcement learning 327

The life cycle of reinforcement learning 329

Deep learning approaches to reinforcement learning 349

Use cases for reinforcement learning 350

Index 355

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