The Applied Artificial Intelligence Workshop

封面

版权信息

Preface

1. Introduction to Artificial Intelligence

Introduction

Fields and Applications of AI

AI Tools and Learning Models

The Role of Python in AI

Python for Game AI

Heuristics

Pathfinding with the A* Algorithm

Introducing the A* Algorithm

Game AI with the Minmax Algorithm and Alpha-Beta Pruning

The Minmax Algorithm

DRYing Up the Minmax Algorithm – the NegaMax Algorithm

Summary

2. An Introduction to Regression

Introduction

Linear Regression with One Variable

Linear Regression with Multiple Variables

Polynomial and Support Vector Regression

Support Vector Regression

Summary

3. An Introduction to Classification

Introduction

The Fundamentals of Classification

Data Preprocessing

The K-Nearest Neighbors Classifier

Classification with Support Vector Machines

Summary

4. An Introduction to Decision Trees

Introduction

Decision Trees

The Confusion Matrix

Random Forest Classifier

Summary

5. Artificial Intelligence: Clustering

Introduction

Defining the Clustering Problem

Clustering Approaches

The K-Means Algorithm

The Mean Shift Algorithm

Clustering Performance Evaluation

Summary

6. Neural Networks and Deep Learning

Introduction

Artificial Neurons

Neurons in TensorFlow

Neural Network Architecture

Activation Functions

Forward Propagation and the Loss Function

Backpropagation

Optimizers and the Learning Rate

Regularization

Deep Learning

Computer Vision and Image Classification

Recurrent Neural Networks (RNNs)

Summary

Appendix

1. Introduction to Artificial Intelligence

2. An Introduction to Regression

3. An Introduction to Classification

4. An Introduction to Decision Trees

5. Artificial Intelligence: Clustering