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

Convolutional Neural Networks


Another important set of neural networks in deep learning is CNN. They are designed specifically for image recognition and classification. CNNs have multiple layers of neural networks that extract information from images and determine the class they fall into.

For example, a CNN can detect whether the image is a cat or not if it is trained with a set of images of cats. We will see the architecture and working of CNN in this section.

For a program, any image is a just a set of RGB numbers in a vector format. If we can make a neural network understand the pattern, it can form a CNN and detect images.

Regular neural nets are universal mathematical approximators that take an input, transform it through a series of functions, and derive the output. However, these regular neural networks do not scale well for an image analysis. For a 32 x 32 pixel RGB image, the hidden layer would have 32*32*3=3072 weights. The regular neural nets work fine for this case. However, when...