How beneficial is dropout regularization in deep learning models? Does it speed up or slow down the training process, and why?

The dropout regularization method mostly proves beneficial for cases where the dataset is small, and a deep neural network is likely to overfit during training. The computational factor has to be considered for large datasets, which may outweigh the benefit of dropout regularization. The dropout regularization method involves the random removal of a layer from a deep neural network, which speeds up the training process.