Explain Dropout in Neural Networks.

Explain Dropout in Neural Networks.

Approach: Describe randomly disabling neurons during training.

A regularization technique for neural networks. During training, a random fraction of neurons in a layer are 'dropped out' (ignored) in each iteration. This prevents neurons from co-adapting too much and makes the network more robust.

Follow-up: Is dropout used during testing/inference?

Key concepts: overfitting, co-adaptation, ensemble.