Anomaly Detection is a big scientific domain, and with such big domains, come many associated techniques and tools. The autoencoder is one of those tools and the subject of this walk-through. H2O offers an easy to use, unsupervised and non-linear
autoencoder as part of its
deeplearning model. Autoencoding mostly aims at reducing feature space in order to distill the essential aspects of the data versus more conventional deeplearning which blows up the feature space up to capture non-linearities and subtle interactions within the data. Autoencoding can also be seen as a non-linear alternative to PCA.
This is the data set we will use to run our models. Let’s go get it and assign it to