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
About the Authors
About the Reviewer
Customer Feedback


This chapter will get you started with deep learning and help you set up your systems to develop deep learning models. The chapter is more focused on giving the audience a heads-up on what is expected from the book and the prerequisites required to go through the book. The current book is intended for students or professionals who want to quickly build a background in the applications of deep learning. The book will be more practical and application-focused using R as a tool to build deep learning models.


For a detailed theory on deep learning, refer to Deep Learning by Goodfellow et al. 2016. For a machine learning background refer Python Machine Learning by S. Raschka, 2015.

We will use the R programming language to demonstrate applications of deep learning. You are expected to have the following prerequisites throughout the book:

  • Basic R programming knowledge
  • Basic understanding of Linux; we will use the Ubuntu (16.04) operating system
  • Basic understanding of machine learning concepts
  • For Windows or macOS, a basic understanding of Docker