What is an anomaly and why is detection important?
Machine learning and artificial intelligence are used to detect abnormal activity using supervised
These anomaly detection systems require data analysts to categorize data points as normal or abnormal for training. A machine learning model trained with labeled data may identify outliers from examples
Unsupervised learning uses deep learning and neural networks or auto encoders that replicate biological neuron signals
Semi-supervised anomaly detection approaches combine the benefits of the preceding two. Engineers can automate feature learning and work with unstructured data using unsupervised learning
Anomaly detection helps businesses across industries perform. Data type and operational challenge determine the employment of supervised, unsupervised, and semi-supervised learning techniques