Dropout Regularization

Dropout regularization is a technique used in neural networks to prevent overfitting during training. By randomly dropping a subset of neurons during each training iteration, dropout forces the network to learn more robust features that are not reliant on any single neuron. This technique is particularly effective in deep learning models, where complex architectures can easily overfit to training data. Commonly used in various applications, dropout helps improve the generalization of models in tasks such as image classification and natural language processing.