Chapter 9. Anomaly Detection and Recommendation Systems

This chapter will look at auto-encoder models and recommendation systems. Although these two use cases may seem very different, they both rely on finding different representations of data. These representations are similar to the embeddings we saw in Chapter 7, Natural Language Processing Using Deep Learning. The first part of this chapter introduces unsupervised learning where there is no specific outcome to be predicted. The next section provides a conceptual overview of auto-encoder models in a machine learning and deep neural network context in particular. We will show you how to build and apply an auto-encoder model to identify anomalous data. Such atypical data may be bad data or outliers, but could also be instances that require further investigation, for example, fraud detection. An example of applying anomaly detection is detecting when an individual's credit card spending pattern differs from their usual behavior. Finally...