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

Application using text2vec examples


In this section, we will analyze the performance of logistic regression on various examples of text2vec.

How to do it...

Here is how we apply text2vec:

  1. Load the required packages and dataset:
library(text2vec) 
library(glmnet) 
data("movie_review") 
  1. Function to perform Lasso logistic regression, and return the train and test AUC values:
logistic_model <- function(Xtrain,Ytrain,Xtest,Ytest)
{ 
  classifier <- cv.glmnet(x=Xtrain, y=Ytrain, 
  family="binomial", alpha=1, type.measure = "auc", 
  nfolds = 5, maxit = 1000) 
  plot(classifier) 
  vocab_test_pred <- predict(classifier, Xtest, type = "response") 
  return(cat("Train AUC : ", round(max(classifier$cvm), 4), 
  "Test AUC : ",glmnet:::auc(Ytest, vocab_test_pred),"\n")) 
} 
  1. Split the movies review data into train and test in an 80:20 ratio:
train_samples <- caret::createDataPartition(c(1:length(labels[1,1])),p = 0.8)$Resample1 
train_movie <- movie_review[train_samples,] 
test_movie <- movie_review...