Enhancing Deep Learning Models with Dropout: A Strategy to Combat Overfitting

Dropout is a regularization technique used in deep learning to prevent overfitting. It randomly drops out a number of output features of the layer during training and forces the network to learn more robust features. It reduces overfitting and improves model performance. However, tuning the dropout rate and increased training time can be challenges.