Anomaly detection is the process of identifying items, events, or occurrences that have different characteristics from the majority of the data. It has many applications in various fields, like fraud detection for credit cards, insurance, or healthcare; network intrusion detection for cybersecurity; KPI metrics monitoring for critical systems; and predictive maintenance for in-service equipment. There are four main categories of techniques to detect anomalies: Classification, nearest neighbor, clustering, and statistical. In this post, we focus on a deep learning statistical anomaly detection approach using variational autoencoders.
Deep learning is a sub-field of machine learning (ML) and has been rapidly growing …
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