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


A lot of development has happened within the deep learning domain in recent years, to enhance algorithmic efficacy and computational efficiency across different domains such as text, images, audio, and video. However, when it comes to training on new datasets, machine learning usually rebuilds the model from scratch, as is done in traditional data science problem solving. This becomes challenging when a new big dataset need to be trained as it will require very high computation power a lot of and time to reach the desired model efficacy.

Transfer Learning is a mechanism to learn new scenarios from existing models. This approach is very useful to train on big datasets, not necessarily from a similar domain or problem statement. For example, researchers have shown examples of Transfer Learning where they have trained Transfer Learning for completely different problem scenarios, such as when a model built using classifications of cat and dog is used for classifying objects such...