How models actually learn, from vanilla gradient descent to Adam
Regularization is often introduced as a penalty added to the loss. Geometrically, it changes which parameter vectors are considered cheap or expensive. That changes the shape of the optimization problem. Two symbols recur below: R(θ) names the penalty term, and λ (lambda) sets how strongly it counts.
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▶ Regularization as Geometry