Compared to using PCA for dimensionality reduction, using a neural autoencoder has the big advantage that it works with source data that contains both numeric and categorical data, while PCA works ...

The neural autoencoder anomaly detection technique presented in this article is just one of many ways to look for data anomalies. The technique assumes you are working with tabular data, such as log ...

Also, feature detection is only one step in understanding the neural network, and much work is needed to understand it further.

This data was then fed into a neural network—a variational autoencoder—that identified key patterns, discarded irrelevant information, and generated a set of characteristics describing the ...