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)

Buy this Book

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

TensorFlow for Python

TensorFlow is one of the most important machine learning and open source libraries maintained by Google. The TensorFlow API is available in many languages, including Python, JavaScript, Java, and C. As TensorFlow supports supervised learning, we will use TensorFlow for building a graph model, and then use this model for prediction.

TensorFlow works with tensors. Some examples for tensors are:

Scalar values such as a floating point number.
A vector of arbitrary length.
A regular matrix, containing p times q values, where p and q are finite integers.
A p x q x r generalized matrix-like structure, where p, q, r are finite integers. Imagine this construct as a rectangular object in three dimensional space with sides p, q, and r. The numbers in this data structure can be visualized in three dimensions.
Observing the above four data structures, more complex, n-dimensional data structures can also be valid examples for tensors.

We...

Artificial Intelligence and Machine Learning Fundamentals

By : Nagy

Artificial Intelligence and Machine Learning Fundamentals

By: Nagy

Overview of this book

TensorFlow for Python

Confirmation

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