What is Regularization in Deep Learning and how does it prevent overfitting?

Regularization is a set of techniques used to reduce overfitting by constraining model complexity and improving generalization.

Deep learning models often have high capacity, making them prone to overfitting training data.

Regularization techniques introduce constraints to improve generalization.

Core methods:

Why it works:

Mathematical intuition: Loss = Data Loss + λ * Regularization Term…