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 H2O


In this section, we will cover the application of H2O in setting up a neural network. The example will use a similar dataset as used in logistic regression.

Getting ready

We first load all the required packages with the following code:

# Load the required packages
require(h2o)

Then, initialize a single-node H2O instance using the h2o.init() function on eight cores and instantiate the corresponding client module on the IP address localhost and port number 54321:

# Initialize H2O instance (single node)
localH2O = h2o.init(ip = "localhost", port = 54321, startH2O = TRUE,min_mem_size = "20G",nthreads = 8)

How to do it...

The section shows how to build neural network using H20.

  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)...