Artificial Intelligence & Machine Learning
Dropout
Definition
Dropout is a regularization technique for neural networks that prevents overfitting. It works by randomly "dropping out" (ignoring) a fraction of neurons during each training step. This forces the network to learn more robust features that are not dependent on any single neuron.
Why It Matters
Dropout is a simple but powerful and widely used technique to combat overfitting in deep learning. It helps models generalize better to new data.
Contextual Example
During training, a dropout layer might be set to randomly ignore 20% of the neurons that pass through it on each forward pass. This prevents the network from becoming too reliant on specific neurons.
Common Misunderstandings
- Dropout is only used during training. During inference (testing), all neurons are used.
- The "dropout rate" (the fraction of neurons to drop) is a hyperparameter.