Dropout regularization is a technique used in deep learning and neural networks to prevent overfitting by randomly dropping out a proportion of neurons during training. This method forces the network to learn more robust features that are useful even when some neurons are not active, promoting better generalization to new data. By creating a different architecture for each training iteration, dropout helps in making the model less sensitive to the specific weights of individual neurons.
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