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by Anthony So, William So, Zsolt Nagy
The Applied Artificial Intelligence Workshop
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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