In Chapter 4, Unsupervised Feature Learning, we saw the mechanisms of feature learning and in particular the use of auto-encoders as an unsupervised pre-training step for supervised learning tasks.
In this chapter, we are going to apply similar concepts, but for a different use case, anomaly detection.
One of the determinants for a good anomaly detector is finding smart data representations that can easily evince deviations from the normal distribution. Deep auto-encoders work very well in learning high-level abstractions and non-linear relationships of the underlying data. We will show how deep learning is a great fit for anomaly detection.
In this chapter, we will start by explaining the differences and communalities of concepts between outlier detection and anomaly detection. The reader will be guided through an imaginary fraud case study followed by examples showing the danger of having anomalies in real-world applications and the importance of automated and...