Glossary › Dropout
Dropout
A regularization technique where a random fraction of neurons are set to zero during training. This prevents neurons from co-adapting too closely and forces more robust feature representations. Applied during training only - at inference, all neurons are active and outputs are scaled by the keep probability.
Where Dropout shows up
All matching postsRegularization: L1, L2, and Dropout Without the Formulas
Models that overfit memorize training data instead of learning patterns. Regularization prevents this. This post explains the three main techniques and when to use each.
#deep-learning#sklearn#gradient-descent