We will be using the AE implementation available in H2O for our project. H2O is a fully open source, distributed, in-memory ML platform with linear scalability. It offers parallelized implementations of some of the most widely used ML algorithms. It supports an easy to use, unsupervised, and non-linear AE as part of its deep learning model. The DL AE of H2O is based on the multilayer neural net architecture, where the entire network is trained together, instead of being stacked layer by layer.

The h2o package can be installed in R with the following command:

install.packages("h2o")

Once the package is installed successfully, the functions offered by the h2o package, including the AE, can simply be used by including the following line in R code:

library(h2o)

This is all we need to do prior to coding our credit card fraud detection system with the AE. Without waiting any longer, let's start building our code to explore and prepare our dataset, as well as to implement the AE that captures fraudulent credit card transactions.