Grokking Artificial Intelligence Algorithms Table of Contents
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