Hands-On Deep Learning with R

Preparing a sample dataset

For Keras, H2O, and MXNet, we will use the adult census dataset, which uses U.S. Census data to predict whether someone makes more or less than USD50,000 a year. We will perform the data preparation for the Keras and MXNet examples here, so we are not repeating the same code in both examples:

1. In the following code, we will load the data and label the two datasets to prepare for combining them:

library(tidyverse)library(caret)train <- read.csv("adult_processed_train.csv")train <- train %>% dplyr::mutate(dataset = "train")test <- read.csv("adult_processed_test.csv")test <- test %>% dplyr::mutate(dataset = "test")

As a result of running the preceding code, we will now have our libraries loaded and ready to use. We ...