Cover image for Grokking Artificial Intelligence Algorithms

Grokking Artificial Intelligence Algorithms

Author Rishal Hurbans
Release Date: 2020/08/01
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Book Description

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

  1. Grokking Artificial Intelligence Algorithms
  2. Copyright
  3. contents
  4. dedication
  5. preface
    1. Our obsession with technology and automation
    2. Ethics, legal matters, and our responsibility
      1. Intention and impact: Understanding your vision and goals
      2. Unintended use: Protecting against malicious use
      3. Unintended bias: Building solutions for everyone
      4. The law, privacy, and consent: Knowing the importance of core values
      5. Singularity: Exploring the unknown
    3. Who should read this book
    4. How this book is organized: A roadmap
    5. About the Code
    6. liveBook discussion forum
    7. Other online resources
  6. 1 Intuition of artificial intelligence
    1. What is artificial intelligence?
      1. Defining AI
      2. Understanding that data is core to AI algorithms
      3. Viewing algorithms as instructions in recipes
    2. A brief history of artificial intelligence
    3. Problem types and problem-solving paradigms
      1. Search problems: Find a path to a solution
      2. Optimization problems: Find a good solution
      3. Prediction and classification problems: Learn from patterns in data
      4. Clustering problems: Identify patterns in data
      5. Deterministic models: Same result each time it’s calculated
      6. Stochastic/probabilistic models: Potentially different result each time it’s calculated
    4. Intuition of artificial intelligence concepts
      1. Narrow intelligence: Specific-purpose solutions
      2. General intelligence: Humanlike solutions
      3. Super intelligence: The great unknown
      4. Old AI and new AI
      5. Search algorithms
      6. Biology-inspired algorithms
      7. Machine learning algorithms
      8. Deep learning algorithms
    5. Uses for artificial intelligence algorithms
      1. Agriculture: Optimal plant growth
      2. Banking: Fraud detection
      3. Cybersecurity: Attack detection and handling
      4. Health care: Diagnosis of patients
      5. Logistics: Routing and optimization
      6. Telecoms: Optimizing networks
      7. Games: Creating AI agents
      8. Art: Creating masterpieces
    6. Summary of Intuition of artificial intelligence
  7. 2 Search fundamentals
    1. What are planning and searching?
    2. Cost of computation: The reason for smart algorithms
    3. Problems applicable to searching algorithms
    4. Representing state: Creating a framework to represent problem spaces and solutions
      1. Graphs: Representing search problems and solutions
      2. Representing a graph as a concrete data structure
      3. Trees: The concrete structures used to represent search solutions
    5. Uninformed search: Looking blindly for solutions
    6. Breadth-first search: Looking wide before looking deep
    7. Depth-first search: Looking deep before looking wide
    8. Use cases for uninformed search algorithms
    9. Optional: More about graph categories
    10. Optional: More ways to represent graphs
      1. Incidence matrix
      2. Adjacency list
    11. Summary of search fundamentals
  8. 3 Intelligent search
    1. Defining heuristics: Designing educated guesses
    2. Informed search: Looking for solutions with guidance
      1. A* search
      2. Use cases for informed search algorithms
    3. Adversarial search: Looking for solutions in a changing environment
      1. A simple adversarial problem
      2. Min-max search: Simulate actions and choose the best future
      3. Alpha-beta pruning: Optimize by exploring the sensible paths only
      4. Use cases for adversarial search algorithms
    4. Summary of Intelligent search
  9. 4 Evolutionary algorithms
    1. What is evolution?
    2. Problems applicable to evolutionary algorithms
    3. Genetic algorithm: Life cycle
    4. Encoding the solution spaces
      1. Binary encoding: Representing possible solutions with zeros and ones
    5. Creating a population of solutions
    6. Measuring fitness of individuals in a population
    7. Selecting parents based on their fitness
      1. Steady state: Replacing a portion of the population each generation
      2. Generational: Replacing the entire population each generation
      3. Roulette wheel: Selecting parents and surviving individuals
    8. Reproducing individuals from parents
      1. Single-point crossover: Inheriting one part from each parent
      2. Two-point crossover: Inheriting more parts from each parent
      3. Uniform crossover: Inheriting many parts from each parent
      4. Bit-string mutation for binary encoding
      5. Flip-bit mutation for binary encoding
    9. Populating the next generation
      1. Exploration vs. exploitation
      2. Stopping conditions
    10. Configuring the parameters of a genetic algorithm
    11. Use cases for evolutionary algorithms
    12. Summary of evolutionary algorithms
  10. 5 Advanced evolutionary approaches
    1. Evolutionary algorithm life cycle
    2. Alternative selection strategies
      1. Rank selection: Even the playing field
      2. Tournament selection: Let them fight
      3. Elitism selection: Choose only the best
    3. Real-value encoding: Working with real numbers
      1. Real-value encoding at its core
      2. Arithmetic crossover: Reproduce with math
      3. Boundary mutation
      4. Arithmetic mutation
    4. Order encoding: Working with sequences
      1. Importance of the fitness function
      2. Order encoding at its core
      3. Order mutation: Order/permutation encoding
    5. Tree encoding: Working with hierarchies
      1. Tree encoding at its core
      2. Tree crossover: Inheriting portions of a tree
      3. Change node mutation: Changing the value of a node
    6. Common types of evolutionary algorithms
      1. Genetic programming
      2. Evolutionary programming
    7. Glossary of evolutionary algorithm terms
    8. More use cases for evolutionary algorithms
    9. Summary of advanced evolutionary approaches
  11. 6 Swarm intelligence: Ants
    1. What is swarm intelligence?
    2. Problems applicable to ant colony optimization
    3. Representing state: What do paths and ants look like?
    4. The ant colony optimization algorithm life cycle
      1. Initialize the pheromone trails
      2. Set up the population of ants
      3. Choose the next visit for each ant
      4. Update the pheromone trails
      5. Update the best solution
      6. Determine the stopping criteria
    5. Use cases for ant colony optimization algorithms
    6. Summary of ant colony optimization
  12. 7 Swarm intelligence: Particles
    1. What is particle swarm optimization?
    2. Optimization problems: A slightly more technical perspective
    3. Problems applicable to particle swarm optimization
    4. Representing state: What do particles look like?
    5. Particle swarm optimization life cycle
      1. Initialize the population of particles
      2. Calculate the fitness of each particle
      3. Update the position of each particle
      4. Determine the stopping criteria
    6. Use cases for particle swarm optimization algorithms
    7. Summary of particle swarm optimization
  13. 8 Machine learning
    1. What is machine learning?
    2. Problems applicable to machine learning
      1. Supervised learning
      2. Unsupervised learning
      3. Reinforcement learning
    3. A machine learning workflow
      1. Collecting and understanding data: Know your context
      2. Preparing data: Clean and wrangle
      3. Training a model: Predict with linear regression
      4. Testing the model: Determine the accuracy of the model
      5. Improving accuracy
    4. Classification with decision trees
      1. Classification problems: Either this or that
      2. The basics of decision trees
      3. Training decision trees
      4. Classifying examples with decision trees
    5. Other popular machine learning algorithms
    6. Use cases for machine learning algorithms
    7. Summary of machine learning
  14. 9 Artificial neural networks
    1. What are artificial neural networks?
    2. The Perceptron: A representation of a neuron
    3. Defining artificial neural networks
    4. Forward propagation: Using a trained ANN
    5. Backpropagation: Training an ANN
      1. Phase A: Setup
      2. Phase B: Forward propagation
      3. Phase C: Training
    6. Options for activation functions
    7. Designing artificial neural networks
      1. Inputs and outputs
      2. Hidden layers and nodes
      3. Weights
      4. Bias
      5. Activation functions
      6. Cost function and learning rate
    8. Artificial neural network types and use cases
      1. Convolutional neural network
      2. Recurrent neural network
      3. Generative adversarial network
    9. Summary of artificial neural networks
  15. 10 Reinforcement learning with Q-learning
    1. What is reinforcement learning?
      1. The inspiration for reinforcement learning
    2. Problems applicable to reinforcement learning
    3. The life cycle of reinforcement learning
      1. Simulation and data: Make the environment come alive
      2. Training with the simulation using Q-learning
      3. Testing with the simulation and Q-table
      4. Measuring the performance of training
      5. Model-free and model-based learning
    4. Deep learning approaches to reinforcement learning
    5. Use cases for reinforcement learning
      1. Robotics
      2. Recommendation engines
      3. Financial trading
      4. Game playing
    6. Summary of reinforcement learning
  16. index
  17. RELATED MANNING TITLES
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