Python Deep Learning

Chapter 9. Anomaly Detection

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 ...