Anomaly Detection

Catastrophic Forgetting

The tendency of neural networks to completely lose previously learned knowledge when trained on new tasks, a fundamental challenge in continual and multi-task learning.

A training strategy that presents examples to a model in a meaningful order, typically from easy to hard.

A mathematical function that measures the difference between predicted outputs and actual target values during model training.

A dimensionality reduction technique that transforms data into orthogonal components ordered by the amount of variance they explain.

A partitioning algorithm that divides data into k clusters by minimising the distance between points and their cluster centroids.

A recommendation technique that makes predictions based on the collective preferences and behaviour of many users.

Learning to learn — algorithms that improve their learning process by leveraging experience from multiple learning episodes.

Continuous observation of deployed machine learning models to detect performance degradation, data drift, anomalous predictions, and infrastructure issues in production.

One complete pass through the entire training dataset during the machine learning model training process.