Loss Function

A scalar measure optimized during training, typically expected loss over data, sometimes with regularization terms.

Measures divergence between true and predicted probability distributions.

Average of squared residuals; common regression objective.

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 conceptual framework describing error as the sum of systematic error (bias) and sensitivity to data (variance).

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.

The learned numeric values of a model adjusted during training to minimize a loss function.