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 …
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.
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.
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.
For software developers with high school–level algebra and calculus skills.
Rishal Hurbans is a technologist, founder, and international speaker.
This book takes an impossibly broad area of computer science and communicates what working developers need to understand in a clear and thorough way.Preface - Our obsession with technology and automation
Preface - Ethics, legal matters, and our responsibility
Chapter 1 Intuition of artificial intelligence
Chapter 1 A brief history of artificial intelligence
Chapter 1 Super intelligence: The great unknown
Chapter 1 Banking: Fraud detection
Chapter 2 Search fundamentals
Chapter 2 Representing state: Creating a framework to represent problem spaces and solutions
Chapter 2 Breadth-first search: Looking wide before looking deep
Chapter 2 Depth-first search: Looking deep before looking wide
Chapter 3 Intelligent search
Chapter 3 A* search
Chapter 3 Use cases for informed search algorithms
Chapter 3 Exercise: What values would propagate in the following Min-max tree?
Chapter 3 Alpha-beta pruning: Optimize by exploring the sensible paths only
Chapter 4 Evolutionary algorithms
Chapter 4 Problems applicable to evolutionary algorithms
Chapter 4 Encoding the solution spaces
Chapter 4 Selecting parents based on their fitness
Chapter 4 Two-point crossover: Inheriting more parts from each parent
Chapter 4 Configuring the parameters of a genetic algorithm
Chapter 5 Advanced evolutionary approaches
Chapter 5 Arithmetic crossover: Reproduce with math
Chapter 5 Change node mutation: Changing the value of a node
Chapter 6 Swarm intelligence: Ants
Chapter 6 Representing state: What do paths and ants look like?
Chapter 6 Set up the population of ants
Chapter 6 Updating pheromones based on ant tours
Chapter 7 Swarm intelligence: Particles
Chapter 7 Problems applicable to particle swarm optimization
Chapter 7 Calculate the fitness of each particle
Chapter 7 Position update
Chapter 8 Machine learning
Chapter 8 Collecting and understanding data: Know your context
Chapter 8 Ambiguous values
Chapter 8 Finding the mean of the features
Chapter 8 Testing the model: Determine the accuracy of the model
Chapter 8 Classification with decision trees
Chapter 8 Decision-tree learning life cycle
Chapter 8 Classifying examples with decision trees
Chapter 9 Artificial neural networks
Chapter 9 Exercise: Calculate the output of the following input for the Perceptron
Chapter 9 Forward propagation: Using a trained ANN
Chapter 9 Backpropagation: Training an ANN
Chapter 9 Options for activation functions
Chapter 9 Bias
Chapter 10 Reinforcement learning with Q-learning
Chapter 10 Problems applicable to reinforcement learning
Chapter 10 Training with the simulation using Q-learning
Chapter 10 Exercise: Calculate the change in values for the Q-table
Chapter 10 Deep learning approaches to reinforcement learning