Model

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.

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.

The relationship between inputs and outputs changes over time, requiring monitoring and model updates.

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...