Definition¶
Tags¶
Optimization, Training
References¶
Additional Notes¶
Dropout is a regularization technique used in neural networks to prevent overfitting. Here’s how it works:
During training, dropout randomly “turns off” (sets to zero) a certain percentage of neurons in a layer with each training batch. Typically, you might drop out 20-50% of neurons. When a neuron is dropped out, it doesn’t participate in forward propagation or backpropagation for that specific training batch.
Think of it like forcing the network to learn with an incomplete brain each time. This has several beneficial effects:
During inference (when actually using the model), dropout is turned off and all neurons are active. To compensate for having more active neurons than during training, the weights are typically scaled proportionally.
A helpful analogy is to think of dropout like studying for an exam where you know some of your study materials will be unavailable during the test. This forces you to develop a more robust understanding rather than relying on any single source too heavily.
Interestingly, this technique was partly inspired by sexual reproduction in nature, where genes are randomly combined from two parents, forcing useful genes to work well in many different combinations rather than relying on specific gene combinations.