Book Image

Neural Networks with R

By : Balaji Venkateswaran, Giuseppe Ciaburro
Book Image

Neural Networks with R

By: Balaji Venkateswaran, Giuseppe Ciaburro

Overview of this book

Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you’ll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book.
Table of Contents (14 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Supervised learning in neural networks


As previously mentioned, supervised learning is a learning method where there is a part of training data which acts as a teacher to the algorithm to determine the model. In the following section, an example of a regression predictive modeling problem is proposed to understand how to solve it with neural networks.

Boston dataset

The dataset describes 13 numerical properties of houses in Boston suburbs, and is concerned with modeling the price of houses in those suburbs in thousands of dollars. As such, this is a regression predictive modeling problem. Input attributes include things like crime rate, proportion of non-retail business acres, chemical concentrations, and more. In the following list are shown all the variables followed by a brief description: 

  • Number of instances: 506
  • Number of attributes: 13 continuous attributes (including class attribute MEDV), and one binary-valued attribute

Each of the attributes is detailed as follows:

  1. crim per capita crime...