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 a neural network using MXNet


The previous chapter provided the details for the installation of MXNet in R along with a working example using its web interface. To start modeling, load the MXNet package in the R environment.

Getting ready

Load the required packages:

# Load the required packages
require(mxnet)

How to do it...

  1. Load the occupancy train and test datasets in R:
# Load the occupancy data 
occupancy_train <-read.csv("C:/occupation_detection/datatraining.txt",stringsAsFactors = T)
occupancy_test <- read.csv("C:/occupation_detection/datatest.txt",stringsAsFactors = T)
  1. The following independent (x) and dependent (y) variables will be used to model GLM:
# Define input (x) and output (y) variables
x = c("Temperature", "Humidity", "Light", "CO2", "HumidityRatio")
y = "Occupancy"
  1. Based on the requirement by MXNet, convert the train and test datasets to a matrix and ensure that the class of the outcome variable is numeric (instead of factor as in the case of H2O):
# convert the train...