What is Model Overparameterization and why do large Deep Learning models still generalize well?

Overparameterization refers to neural networks having more parameters than training samples, yet they can still generalize well due to implicit regularization and optimization dynamics.

Traditional machine learning theory suggests that models with too many parameters should overfit. However, deep learning challenges this intuition.

Modern neural networks are often heavily overparameterized, yet they generalize effectively.

Why this happens:

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