What is Dropout and how does it improve generalization in Neural Networks?

Dropout is a regularization technique that randomly deactivates neurons during training to prevent co-adaptation and improve generalization.

Neural networks can overfit by relying too heavily on specific neurons, causing brittle representations that fail on unseen data.

Dropout prevents this by introducing stochastic neuron removal during training.

Mechanism:

Effect:

Why it works: