The Applied Artificial Intelligence Workshop

Spis treści książki

• The Applied Artificial Intelligence Workshop
• Preface
  • About the Book
    • Audience
    • About the Chapters
    • Conventions
    • Code Presentation
    • Setting up Your Environment
    • Installing Jupyter on Your System
    • Launching the Jupyter Notebook
    • Installing Libraries
    • A Few Important Packages
    • Accessing the Code Files
• 1. Introduction to Artificial Intelligence
  • Introduction
    • How Does AI Solve Problems?
    • Diversity of Disciplines in AI
  • Fields and Applications of AI
    • Simulation of Human Behavior
    • Simulating Intelligence the Turing Test
      • What Disciplines Do We Need to Pass the Turing Test?
  • AI Tools and Learning Models
    • Intelligent Agents
  • The Role of Python in AI
    • Why Is Python Dominant in Machine Learning, Data Science, and AI?
    • Anaconda in Python
    • Python Libraries for AI
    • A Brief Introduction to the NumPy Library
    • Exercise 1.01: Matrix Operations Using NumPy
  • Python for Game AI
    • Intelligent Agents in Games
    • Breadth First Search and Depth First Search
      • Breadth First Search (BFS)
      • Depth First Search (DFS)
    • Exploring the State Space of a Game
    • Estimating the Number of Possible States in a Tic-Tac-Toe Game
    • Exercise 1.02: Creating an AI with Random Behavior for the Tic-Tac-Toe Game
    • Activity 1.01: Generating All Possible Sequences of Steps in a Tic-Tac-Toe Game
    • Exercise 1.03: Teaching the Agent to Win
    • Defending the AI against Losses
    • Activity 1.02: Teaching the Agent to Realize Situations When It Defends Against Losses
    • Activity 1.03: Fixing the First and Second Moves of the AI to Make It Invincible
  • Heuristics
    • Uninformed and Informed Searches
    • Creating Heuristics
    • Admissible and Non-Admissible Heuristics
    • Heuristic Evaluation
      • Heuristic 1: Simple Evaluation of the Endgame
      • Heuristic 2: Utility of a Move
    • Exercise 1.04: Tic-Tac-Toe Static Evaluation with a Heuristic Function
    • Using Heuristics for an Informed Search
    • Types of Heuristics
  • Pathfinding with the A* Algorithm
    • Exercise 1.05: Finding the Shortest Path Using BFS
  • Introducing the A* Algorithm
    • A* Search in Practice Using the simpleai Library
  • Game AI with the Minmax Algorithm and Alpha-Beta Pruning
    • Search Algorithms for Turn-Based Multiplayer Games
  • The Minmax Algorithm
    • Optimizing the Minmax Algorithm with Alpha-Beta Pruning
  • DRYing Up the Minmax Algorithm the NegaMax Algorithm
    • Using the EasyAI Library
    • Activity 1.04: Connect Four
  • Summary
• 2. An Introduction to Regression
  • Introduction
  • Linear Regression with One Variable
    • Types of Regression
    • Features and Labels
    • Feature Scaling
    • Splitting Data into Training and Testing
    • Fitting a Model on Data with scikit-learn
    • Linear Regression Using NumPy Arrays
    • Fitting a Model Using NumPy Polyfit
      • Plotting the Results in Python
    • Predicting Values with Linear Regression
    • Exercise 2.01: Predicting the Student Capacity of an Elementary School
  • Linear Regression with Multiple Variables
    • Multiple Linear Regression
    • The Process of Linear Regression
    • Importing Data from Data Sources
    • Loading Stock Prices with Yahoo Finance
    • Exercise 2.02: Using Quandl to Load Stock Prices
    • Preparing Data for Prediction
    • Exercise 2.03: Preparing the Quandl Data for Prediction
    • Performing and Validating Linear Regression
    • Predicting the Future
  • Polynomial and Support Vector Regression
    • Polynomial Regression with One Variable
    • Exercise 2.04: First-, Second-, and Third-Degree Polynomial Regression
    • Polynomial Regression with Multiple Variables
  • Support Vector Regression
    • Support Vector Machines with a 3-Degree Polynomial Kernel
    • Activity 2.01: Boston House Price Prediction with Polynomial Regression of Degrees 1, 2, and 3 on Multiple Variables
  • Summary
• 3. An Introduction to Classification
  • Introduction
  • The Fundamentals of Classification
    • Exercise 3.01: Predicting Risk of Credit Card Default (Loading the Dataset)
  • Data Preprocessing
    • Exercise 3.02: Applying Label Encoding to Transform Categorical Variables into Numerical Variables
    • Identifying Features and Labels
    • Splitting Data into Training and Testing Using Scikit-Learn
  • The K-Nearest Neighbors Classifier
    • Introducing the K-Nearest Neighbors Algorithm (KNN)
    • Distance Metrics With K-Nearest Neighbors Classifier in Scikit-Learn
      • The Euclidean Distance
    • The Manhattan/Hamming Distance
    • Exercise 3.03: Illustrating the K-Nearest Neighbors Classifier Algorithm in Matplotlib
    • Parameterization of the K-Nearest Neighbors Classifier in scikit-learn
    • Exercise 3.04: K-Nearest Neighbors Classification in scikit-learn
    • Activity 3.01: Increasing the Accuracy of Credit Scoring
  • Classification with Support Vector Machines
    • What Are Support Vector Machine Classifiers?
    • Understanding Support Vector Machines
    • Support Vector Machines in scikit-learn
    • Parameters of the scikit-learn SVM
    • Activity 3.02: Support Vector Machine Optimization in scikit-learn
  • Summary
• 4. An Introduction to Decision Trees
  • Introduction
  • Decision Trees
    • Entropy
    • Exercise 4.01: Calculating Entropy
    • Information Gain
    • Gini Impurity
    • Exit Condition
    • Building Decision Tree Classifiers Using scikit-learn
    • Performance Metrics for Classifiers
    • Exercise 4.02: Precision, Recall, and F1 Score Calculation
    • Evaluating the Performance of Classifiers with scikit-learn
  • The Confusion Matrix
    • Activity 4.01: Car Data Classification
  • Random Forest Classifier
    • Random Forest Classification Using scikit-learn
    • The Parameterization of the Random Forest Classifier
    • Feature Importance
    • Cross-Validation
    • Extremely Randomized Trees
    • Activity 4.02: Random Forest Classification for Your Car Rental Company
  • Summary
• 5. Artificial Intelligence: Clustering
  • Introduction
  • Defining the Clustering Problem
  • Clustering Approaches
    • Clustering Algorithms Supported by scikit-learn
  • The K-Means Algorithm
    • Exercise 5.01: Implementing K-Means in scikit-learn
    • The Parameterization of the K-Means Algorithm in scikit-learn
    • Exercise 5.02: Retrieving the Center Points and the Labels
    • K-Means Clustering of Sales Data
    • Activity 5.01: Clustering Sales Data Using K-Means
  • The Mean Shift Algorithm
    • Exercise 5.03: Implementing the Mean Shift Algorithm
    • The Mean Shift Algorithm in scikit-learn
    • Hierarchical Clustering
    • Agglomerative Hierarchical Clustering in scikit-learn
  • Clustering Performance Evaluation
    • The Adjusted Rand Index
    • The Adjusted Mutual Information
    • The V-Measure, Homogeneity, and Completeness
    • The Fowlkes-Mallows Score
    • The Contingency Matrix
    • The Silhouette Coefficient
    • The Calinski-Harabasz Index
    • The Davies-Bouldin Index
    • Activity 5.02: Clustering Red Wine Data Using the Mean Shift Algorithm and Agglomerative Hierarchical Clustering
  • Summary
• 6. Neural Networks and Deep Learning
  • Introduction
  • Artificial Neurons
  • Neurons in TensorFlow
    • Exercise 6.01: Using Basic Operations and TensorFlow Constants
  • Neural Network Architecture
    • Weights
    • Biases
    • Use Cases for ANNs
  • Activation Functions
    • Sigmoid
    • Tanh
    • ReLU
    • Softmax
    • Exercise 6.02: Activation Functions
  • Forward Propagation and the Loss Function
  • Backpropagation
  • Optimizers and the Learning Rate
    • Exercise 6.03: Classifying Credit Approval
  • Regularization
    • Exercise 6.04: Predicting Boston House Prices with Regularization
    • Activity 6.01: Finding the Best Accuracy Score for the Digits Dataset
  • Deep Learning
    • Shallow versus Deep Networks
  • Computer Vision and Image Classification
    • Convolutional Neural Networks (CNNs)
    • Convolutional Operations
    • Pooling Layer
    • CNN Architecture
    • Activity 6.02: Evaluating a Fashion Image Recognition Model Using CNNs
  • Recurrent Neural Networks (RNNs)
    • RNN Layers
    • The GRU Layer
    • The LSTM Layer
    • Activity 6.03: Evaluating a Yahoo Stock Model with an RNN
    • Hardware for Deep Learning
    • Challenges and Future Trends
  • Summary
• Appendix
  • 1. Introduction to Artificial Intelligence
    • Activity 1.01: Generating All Possible Sequences of Steps in a Tic-Tac-Toe Game
    • Activity 1.02: Teaching the Agent to Realize Situations When It Defends Against Losses
    • Activity 1.03: Fixing the First and Second Moves of the AI to Make It Invincible
    • Activity 1.04: Connect Four
  • 2. An Introduction to Regression
    • Activity 2.01: Boston House Price Prediction with Polynomial Regression of Degrees 1, 2, and 3 on Multiple Variables
  • 3. An Introduction to Classification
    • Activity 3.01: Increasing the Accuracy of Credit Scoring
    • Activity 3.02: Support Vector Machine Optimization in scikit-learn
  • 4. An Introduction to Decision Trees
    • Activity 4.01: Car Data Classification
    • Activity 4.02: Random Forest Classification for Your Car Rental Company
  • 5. Artificial Intelligence: Clustering
    • Activity 5.01: Clustering Sales Data Using K-Means
    • Activity 5.02: Clustering Red Wine Data Using the Mean Shift Algorithm and Agglomerative Hierarchical Clustering
  • 6. Neural Networks and Deep Learning
    • Activity 6.01: Finding the Best Accuracy Score for the Digits Dataset
    • Activity 6.02: Evaluating a Fashion Image Recognition Model Using CNNs
    • Activity 6.03: Evaluating a Yahoo Stock Model with an RNN

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