Dropout is a regularization technique used during the training of neural networks to prevent overfitting. It involves randomly setting a fraction of the input units to zero at each training step, effectively “dropping out” certain neurons. This process forces the network to learn redundant representations, enhancing its ability to generalize to new, unseen data.
Key Features:
- Random Deactivation: During training, a specified percentage of neurons are randomly deactivated, meaning their outputs are set to zero.
- Improved Generalization: By preventing neurons from co-adapting too much, dropout helps the model generalize better to new data, reducing the risk of overfitting.
Applications/Use Cases:
- Preventing Overfitting: Dropout is widely used in training deep neural networks to prevent overfitting, especially when dealing with limited data.
- Enhancing Model Robustness: By forcing the network to learn more robust features, dropout improves the model’s performance on unseen data.