In this chapter, we looked into detecting anomalous and suspicious patterns. We discussed the two fundamental approaches, focusing on library encoding, either positive or negative patterns. Next, we got our hands on two real-life datasets, and we discussed how to deal with unbalanced class distributions and how to perform anomaly detection on time series data.
In the next chapter, we'll dive deeper into patterns and more advanced approaches to building pattern-based classifiers, and discuss how to assign labels to images using deep learning automatically.
