Dropout
Dropout is a regularization technique used in neural networks during training where a fraction of network nodes are randomly deactivated (dropped out) in each forward pass, preventing co-adaptation of features.
Frequently Asked Questions
How does dropout prevent overfitting?▼
By turning off random neurons, it forces the remaining network to learn redundant representations and robust features rather than depending on specific pathways.
Is dropout active during inference?▼
No, dropout is only active during training. During inference, all weights are utilized but scaled to match the training probability distribution.
Quick Facts
- CategoryModel Training
- Key ApplicationNeural network training, overfitting prevention, and model regularization.
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