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.
Summary
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.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
About the technology
Artificial intelligence touches every part of our lives. It powers our shopping and TV recommendations; it informs our medical diagnoses. Embracing this new world means mastering the core algorithms at the heart of AI.
About the book
Grokking Artificial Intelligence Algorithms uses illustrations, exercises, and jargon-free explanations to teach fundamental AI concepts. All you need is the algebra you remember from high school math class. Explore coding challenges like detecting bank fraud, creating artistic masterpieces, and setting a self-driving car in motion.
What's inside
• 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
About the reader
For software developers with high school–level algebra and calculus skills.
About the author
Rishal Hurbans is a technologist, startup and AI group founder, and international speaker.
Author(s): Rishal Hurbans
Edition: 1
Publisher: Manning Publications
Year: 2020
Language: English
Commentary: Vector PDF
Pages: 392
City: Shelter Island, NY
Tags: Artificial Intelligence; Machine Learning; Algorithms; Evolutionary Computations; Swarm Intelligence; Neural Networks; Reinforcement Learning; Decision Trees; Classification; Problem Solving; Optimization; Perceptron; Algorithms Design Techniques; Activation Functions; Search Algorithms
Contents
Preface
Acknowledgments
About this book
About the author
Chapter 1. Intuition of artificial intelligence
What is artificial intelligence?
A brief history of artificial intelligence
Problem types and problem-solving paradigms
Intuition of artificial intelligence concepts
Uses for artificial intelligence algorithms
Chapter 2. Search fundamentals
What are planning and searching?
Cost of computation: The reason for smart algorithms
Problems applicable to searching algorithms
Representing state: Creating a framework to represent problem spaces and solutions
Uninformed search: Looking blindly for solutions
Breadth-first search: Looking wide before looking deep
Depth-first search: Looking deep before looking wide
Use cases for uninformed search algorithms
Optional: More about graph categories
Optional: More ways to represent graphs
Chapter 3. Intelligent search
Defining heuristics: Designing educated guesses
Informed search: Looking for solutions with guidance
Adversarial search: Looking for solutions in a changing environment
Chapter 4. Evolutionary algorithms
What is evolution?
Problems applicable to evolutionary algorithms
Genetic algorithm: Life cycle
Encoding the solution spaces
Creating a population of solutions
Measuring fitness of individuals in a population
Selecting parents based on their fitness
Reproducing individuals from parents
Populating the next generation
Configuring the parameters of a genetic algorithm
Use cases for evolutionary algorithms
Chapter 5. Advanced evolutionary approaches
Evolutionary algorithm life cycle
Alternative selection strategies
Real-value encoding: Working with real numbers
Order encoding: Working with sequences
Tree encoding: Working with hierarchies
Common types of evolutionary algorithms
Glossary of evolutionary algorithm terms
More use cases for evolutionary algorithms
Chapter 6. Swarm intelligence: Ants
What is swarm intelligence?
Problems applicable to ant colony optimization
Representing state: What do paths and ants look like?
The ant colony optimization algorithm life cycle
Use cases for ant colony optimization algorithms
Chapter 7. Swarm intelligence: Particles
What is particle swarm optimization?
Optimization problems: A slightly more technical perspective
Problems applicable to particle swarm optimization
Representing state: What do particles look like?
Particle swarm optimization life cycle
Use cases for particle swarm optimization algorithms
Chapter 8. Machine learning
What is machine learning?
Problems applicable to machine learning
A machine learning workflow
Classification with decision trees
Other popular machine learning algorithms
Use cases for machine learning algorithms
Chapter 9. Artificial neural networks
What are artificial neural networks?
The Perceptron: A representation of a neuron
Defining artificial neural networks
Forward propagation: Using a trained ANN
Backpropagation: Training an ANN
Options for activation functions
Designing artificial neural networks
Artificial neural network types and use cases
Chapter 10. Reinforcement learning with Q-learning
What is reinforcement learning?
Problems applicable to reinforcement learning
The life cycle of reinforcement learning
Deep learning approaches to reinforcement learning
Use cases for reinforcement learning
Index
