Dropout

Regularization technique in machine learning used to prevent overfitting by randomly dropping units during training. This method helps improve the generalization of the model by reducing the likelihood of the model becoming too tailored to the training data. Dropout is particularly beneficial for machine learning practitioners and researchers aiming to enhance the robustness and performance of their models.