Hands-On Transfer Learning with Python

Anomaly detection

Anomaly detection is the task of identifying rare events/observations based on historical data. Anomaly detection is also termed as outlier detection. Anomalies or outliers usually have characteristics such as being infrequent or occurring in short sudden bursts over time.

For such tasks, we provide a historical dataset for the algorithm so it can identify and learn the normal behavior of data in an unsupervised manner. Once learned, the algorithm helps us identify patterns that differ from this learned behavior.