midPyTorch
What is dropout and how does it work?
Updated May 17, 2026
Short answer
Dropout randomly disables neurons during training to prevent overfitting.
Deep explanation
It forces redundancy in representation by dropping activations with probability p during training.
Real-world example
Used in fully connected layers of deep networks.
Common mistakes
- Using dropout during evaluation mode.
Follow-up questions
- What happens in eval mode?
- Why does dropout improve generalization?