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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.From start to finish, the best book to help you learn AI algorithms and recall why and how you use them.
About the Technology
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.About the book
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.
Table of Contents takes you straight to the bookdetailed table of contents
1 Intuition of artificial intelligence
What is artificial intelligence?
Defining AI
Understanding that data is core to AI algorithms
Viewing algorithms as instructions in recipes
A brief history of artificial intelligence
Problem types and problem-solving 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
Narrow intelligence: Specific-purpose solutions
General intelligence: Humanlike solutions
Super intelligence: The great unknown
Old AI and new AI
Search algorithms
Biology-inspired algorithms
Machine learning algorithms
Deep learning algorithms
Uses for artificial intelligence algorithms
Agriculture: Optimal plant growth
Banking: Fraud detection
Cybersecurity: Attack detection and handling
Health care: Diagnosis of patients
Logistics: Routing and optimization
Telecoms: Optimizing networks
Games: Creating AI agents
Art: Creating masterpieces
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
Graphs: Representing search problems and solutions
Representing a graph as a concrete data structure
Trees: The concrete structures used to represent search 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
Incidence matrix
Adjacency list
Summary of search fundamentals
3 Intelligent search
Defining heuristics: Designing educated guesses
Informed search: Looking for solutions with guidance
A* search
Use cases for informed search algorithms
Adversarial search: Looking for solutions in a changing environment
A simple adversarial problem
Min-max search: Simulate actions and choose the best future
Alpha-beta pruning: Optimize by exploring the sensible paths only
Use cases for adversarial search algorithms
Summary of Intelligent search
4 Evolutionary algorithms
What is evolution?
Problems applicable to evolutionary algorithms
Genetic algorithm: Life cycle
Encoding the solution spaces
Binary encoding: Representing possible solutions with zeros and ones
Creating a population of solutions
Measuring fitness of individuals in a population
Selecting parents based on their fitness
Steady state: Replacing a portion of the population each generation
Generational: Replacing the entire population each generation
Roulette wheel: Selecting parents and surviving individuals
Reproducing individuals from parents
Single-point crossover: Inheriting one part from each parent
Two-point crossover: Inheriting more parts from each parent
Uniform crossover: Inheriting many parts from each parent
Bit-string mutation for binary encoding
Flip-bit mutation for binary encoding
Populating the next generation
Exploration vs. exploitation
Stopping conditions
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
Rank selection: Even the playing field
Tournament selection: Let them fight
Elitism selection: Choose only the best
Real-value encoding: Working with real numbers
Real-value encoding at its core
Arithmetic crossover: Reproduce with math
Boundary mutation
Arithmetic mutation
Order encoding: Working with sequences
Importance of the fitness function
Order encoding at its core
Order mutation: Order/permutation encoding
Tree encoding: Working with hierarchies
Tree encoding at its core
Tree crossover: Inheriting portions of a tree
Change node mutation: Changing the value of a node
Common types of evolutionary algorithms
Genetic programming
Evolutionary programming
Glossary of evolutionary algorithm terms
More use cases for evolutionary algorithms
Summary of advanced evolutionary approache
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
Initialize the pheromone trails
Set up the population of ants
Choose the next visit for each ant
Update the pheromone trails
Update the best solution
Determine the stopping criteria
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
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
Supervised learning
Unsupervised learning
Reinforcement learning
A machine learning workflow
Collecting and understanding data: Know your context
Preparing data: Clean and wrangle
Training a model: Predict with linear regression
Testing the model: Determine the accuracy of the model
Improving accuracy
Classification with decision trees
Classification problems: Either this or that
The basics of decision trees
Training decision trees
Classifying examples 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
Phase A: Setup
Phase B: Forward propagation
Phase C: Training
Options for activation functions
Designing artificial neural networks
Inputs and outputs
Hidden layers and nodes
Weights
Bias
Activation functions
Cost function and learning rate
Artificial neural network types and use cases
Convolutional neural network
Recurrent neural network
Generative adversarial network
Summary of artificial neural networks
10 Reinforcement learning with Q-learning
What is reinforcement learning?
The inspiration for reinforcement learning
Problems applicable to reinforcement learning
The life cycle of reinforcement learning
Simulation and data: Make the environment come alive
Training with the simulation using Q-learning
Testing with the simulation and Q-table
Measuring the performance of training
Model-free and model-based learning
Deep learning approaches to reinforcement learning
Use cases for reinforcement learning
Robotics
Recommendation engines
Financial trading
Game playing
Summary of reinforcement learning
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