”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
Author(s): Rishal Hurbans
Publisher: Manning Publications
Year: 2020
Language: English
Pages: 362
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
