Return to Grokking Artificial Intelligence Algorithms

preface

acknowledgments

about this book

about the author

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

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

3. Intelligent search

Defining heuristics: Designing educated guesses

Informed search: Looking for solutions with guidance

Adversarial search: Looking for solutions in a changing environment

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

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

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

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

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

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

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