Book description
Grokking Artificial Intelligence Algorithms uses illustrations, exercises, and jargonfree 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 selfdriving car in motion.Table of contents
 Copyright
 contents
 dedication
 preface

1 Intuition of artificial intelligence
 What is artificial intelligence?
 A brief history of artificial intelligence

Problem types and problemsolving paradigms
 Search problems: Find a path to a solution
 Optimization problems: Find a good solution
 Prediction and classification problems: Learn from patterns in data
 Clustering problems: Identify patterns in data
 Deterministic models: Same result each time it’s calculated
 Stochastic/probabilistic models: Potentially different result each time it’s calculated
 Intuition of artificial intelligence concepts
 Uses for artificial intelligence algorithms
 Summary of Intuition of artificial intelligence

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
 Breadthfirst search: Looking wide before looking deep
 Depthfirst search: Looking deep before looking wide
 Use cases for uninformed search algorithms
 Optional: More about graph categories
 Optional: More ways to represent graphs
 Summary of search fundamentals

3 Intelligent search
 Defining heuristics: Designing educated guesses
 Informed search: Looking for solutions with guidance
 Adversarial search: Looking for solutions in a changing environment
 Summary of Intelligent search

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
 Summary of evolutionary algorithms

5 Advanced evolutionary approaches
 Evolutionary algorithm life cycle
 Alternative selection strategies
 Realvalue 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
 Summary of advanced evolutionary approaches

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
 Summary of ant colony optimization

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
 Initialize the population of particles
 Calculate the fitness of each particle

Update the position of each particle
 The components of updating velocity
 Updating velocity
 Position update
 Exercise: Calculate the new velocity and position for particle 1 given the following information about the particles
 Solution: Calculate the new velocity and position for particle 1 given the following information about the particles
 Determine the stopping criteria
 Use cases for particle swarm optimization algorithms
 Summary of particle swarm optimization

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
 Summary of machine learning

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
 Summary of artificial neural networks

10 Reinforcement learning with Qlearning
 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
 Summary of reinforcement learning
 index
 RELATED MANNING TITLES
Product information
 Title: Grokking Artificial Intelligence Algorithms
 Author(s):
 Release date: August 2020
 Publisher(s): Manning Publications
 ISBN: 9781617296185
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