Dropout is a regularization technique used in neural networks to reduce overfitting
Dropout randomly turns off some neurons for each forward pass
“Turns off” means their activations are 0 so network cannot rely too heavily on any one neuron or feature
In modern frameworks training uses inverted dropout where the remaining activations are scaled up:
dropout makes training noisier on purpose so the final model generalizes better