Deep Learning with Neural Networks | Artificial Intelligence and Machine Learning Fundamentals

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Artificial Intelligence and Machine Learning Fundamentals

By : Nagy

4.3 (110)

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Artificial Intelligence and Machine Learning Fundamentals

4.3 (110)

By: Nagy

Buy this Book

Overview of this book

Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples. As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law. By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills!

Preface

About the Book

Free Chapter

Principles of Artificial Intelligence

Introduction

Fields and Applications of Artificial Intelligence

AI Tools and Learning Models

The Role of Python in Artificial Intelligence

Python for Game AI

Summary

AI with Search Techniques and Games

Introduction

Heuristics

Pathfinding with the A* Algorithm

Game AI with the Minmax Algorithm and Alpha-Beta Pruning

Summary

Regression

Introduction

Linear Regression with One Variable

Linear Regression with Multiple Variables

Polynomial and Support Vector Regression

Summary

Classification

Introduction

The Fundamentals of Classification

Classification with Support Vector Machines

Summary

Using Trees for Predictive Analysis

Introduction to Decision Trees

Random Forest Classifier

Summary

Clustering

Introduction to Clustering

The k-means Algorithm

Mean Shift Algorithm

Summary

Deep Learning with Neural Networks

Introduction

TensorFlow for Python

Introduction to Neural Networks

Deep Learning

Summary

Appendix

Chapter 1: Principles of AI

Chapter 2: AI with Search Techniques and Games

Chapter 4: Classification

Chapter 5: Using Trees for Predictive Analysis

Chapter 6: Clustering

Chapter 7: Deep Learning with Neural Networks

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Introduction to Neural Networks

Neural networks are the newest branch of AI. Neural networks are inspired by how the human brain works. Originally, they were invented in the 1940s by Warren McCulloch and Walter Pitts. The neural network was a mathematical model that was used for describing how the human brain can solve problems.

We will use the phrase artificial neural network when talking about the mathematical model and use biological neural network when talking about the human brain. Artificial neural networks are supervised learning algorithms.

The way a neural network learns is more complex compared to other classification or regression models. The neural network model has a lot of internal variables, and the relationship between the input and output variables may go through multiple internal layers. Neural networks have higher accuracy as compared to other supervised learning algorithms.

Note

Mastering neural networks with TensorFlow is a complex process. The purpose...

Artificial Intelligence and Machine Learning Fundamentals

By : Nagy

Artificial Intelligence and Machine Learning Fundamentals

By: Nagy

Overview of this book

Introduction to Neural Networks

Note

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