Regularization
In plain terms
Techniques that prevent overfitting by penalizing complexity.
Going deeper
Regularization discourages overly complex models by adding penalties or constraints, improving generalization to new data. It curbs memorization.
Under the hood
L1/L2 penalties, dropout, and early stopping are common; balances fit against simplicity.
Examples
L1/L2 penalties, dropout, weight decay, early stopping, generalization.
Think of it like...
Like a coach reining in flashy moves to keep the player reliable in real games.