What is the main function of dropout in a neural network?

The primary function of dropout in a neural network is to prevent overfitting. During training, dropout randomly deactivates a fraction of neurons in each iteration, which contributes to the robustness of the model. This technique forces the network to learn more general features instead of relying on specific neurons, thus ensuring that the model does not memorize the training data but instead learns to generalize from it.

By incorporating dropout, the network becomes less sensitive to the peculiarities of the training data, leading to improved performance when encountering new, unseen datasets. This is particularly important in scenarios where models trained on limited data can easily overfit, capturing noise rather than meaningful patterns. Through this regularization technique, dropout effectively reduces the chance of overfitting and enhances the model's ability to generalize, resulting in better overall predictions and reliability when applied in real-world situations.