Book Image

R Deep Learning Cookbook

By : PKS Prakash, Achyutuni Sri Krishna Rao
Book Image

R Deep Learning Cookbook

By: PKS Prakash, Achyutuni Sri Krishna Rao

Overview of this book

Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance. By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Introduction


Convolution neural networks (CNN) are a category of deep learning neural networks with a prominent role in building image recognition- and natural language processing-based classification models.

Note

The CNN follows a similar architecture to LeNet, which was primarily designed to recognize characters such as numbers, zip codes, and so on. As against artificial neural networks, CNN have layers of neurons arranged in three-dimensional space (width, depth, and height). Each layer transforms a two-dimensional image into a three-dimensional input volume, which is then transformed into a three-dimensional output volume using neuron activation.

Primarily, CNNs are built using three main types of activation layers: convolution layer ReLU, pooling layer, and fully connected layer. The convolution layer is used to extract features (spatial relationship between pixels) from the input vector (of images) and stores them for further processing after computing a dot product with weights (and...