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

Setting up deep learning tools/packages in R


The major deep learning packages are developed in C/C++ for efficiency purposes and wrappers are developed in R to efficiently develop, extend, and execute deep learning models.

A lot of open source deep learning libraries are available. The prominent libraries in this area are as follows:

  • Theano
  • TensorFlow
  • Torch
  • Caffe

There are other prominent packages available on the market such as H2O, CNTK (Microsoft Cognitive Toolkit), darch, Mocha, and ConvNetJS. There are a lot of wrappers that are developed around these packages to support the easy development of deep learning models, such as Keras and Lasagne in Python and MXNet, both supporting multiple languages.

How to do it...

  1. This chapter will cover the MXNet and TensorFlow packages (developed in C++ and CUDA for a highly optimized performance in GPU).
  2. Additionally, the h2o package will be used to develop some deep learning models. The h2o package in R is implemented as a REST API, which connects to the H2O server (it runs as Java Virtual Machines (JVM)). We will provide quick setup instructions for these packages in the following sections