Regularization

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

Randomly zeroing activations during training to reduce co-adaptation and overfitting.

Halting training when validation performance stops improving to 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).

How well a model performs on new data drawn from the same (or similar) distribution as training.

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.

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