Dropout is a regularization technique used to prevent overfitting in neural networks by randomly "dropping" (setting to zero) a fraction of neurons during training. This forces the network to learn redundant representations and prevents the model from relying too heavily on any single neuron.
During each forward pass, dropout randomly disables a certain percentage of neurons, and during backpropagation, it updates only the active neurons. This prevents the model from overfitting to the training data by making it more robust.
Dropout is particularly useful in deep networks where overfitting is common due to the large number of parameters. It is a simple yet effective way to improve generalization.