Parameters

A function measuring prediction error (and sometimes calibration), guiding gradient-based optimization.

Techniques that discourage overly complex solutions to improve generalization (reduce overfitting).

When a model fits noise/idiosyncrasies of training data and performs poorly on unseen data.

When a model cannot capture underlying structure, performing poorly on both training and test data.

A measurable property or attribute used as model input (raw or engineered), such as age, pixel intensity, or token ID.

Designing input features to expose useful structure (e.g., ratios, lags, aggregations), often crucial outside deep le...

The internal space where learned representations live; operations here often correlate with semantics or generative f...

A parameterized mapping from inputs to outputs; includes architecture + learned parameters.