Summary

Anomaly detection is a very common problem that can be found in many applications.

At the start of this chapter, we described a few possible use cases and highlighted the major types and differences according to the context and application requirements.

We briefly covered some of the popular techniques for solving anomaly detection using shallow machine learning algorithms. The major differences can be found in the way features are generated. In shallow machine learning, this is generally a manual task, also called feature engineering. The advantage of using deep learning is that it can automatically learn smart data representations in an unsupervised fashion. Good data representations can substantially help the detection model to spot anomalies.

We have provided an overview of H2O and summarized its functionalities for deep learning, in particular the auto-encoders.

We have implemented a couple of proof-of-concept examples in order to learn how to apply auto-encoders for solving anomaly...