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The applied artificial intelligence workshop.
With knowledge and information shared by experts, take your first steps towards creating scalable AI algorithms and solutions in Python, through practical exercises and engaging activities Key Features Learn about AI and ML algorithms from the perspective of a seasoned data scientist Get practical e...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham, UK :
Packt Publishing,
2020.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Cover
- FM
- Copyright
- Table of Contents
- Preface
- Chapter 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
- 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
- Chapter 2: An Introductionto 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