Get Code Download Data anomaly detection is the process of examining a set of source data to find data items that are different in some way from the majority of the source items. There are many ...

HOLO's stacked sparse autoencoder, enhanced by the DeepSeek model, employs a layer-wise training strategy that progressively captures complex data relationships. The model's sparsity constraint ...

Time-series data represents one of the most challenging data types for businesses and data scientists. The data sets are often very big, change continuously, and are time-sensitive by nature. One ...

If the dataset has no fraud examples, we can use either the outlier detection approach using isolation forest technique or anomaly detection using the neural autoencoder.

There are many different types of anomaly detection techniques. This article explains how to use a neural autoencoder implemented using raw C# to find anomalous data items. Compared to other anomaly ...