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

PCA using H2O


One of the greatest difficulties encountered in multivariate statistical analysis is the problem of displaying a dataset with many variables. Fortunately, in datasets with many variables, some pieces of data are often closely related to each other. This is because they actually contain the same information, as they measure the same quantity that governs the behavior of the system. These are therefore redundant variables that add nothing to the model we want to build. We can then simplify the problem by replacing a group of variables with a new variable that encloses the information content.

PCA generates a new set of variables, among them uncorrelated, called principal components; each main component is a linear combination of the original variables. All principal components are orthogonal to each other, so there is no redundant information. The principal components as a whole constitute an orthogonal basis for the data space. The goal of PCA is to explain the maximum amount...