Dropout is a regularization approach that helps to avoid overfitting and hence improves generalizability (that is, the model predicts correct output for most of the inputs in general, rather than only being limited to the training data set). In general, we should utilize a low dropout value of 20 percent to 50 percent of neurons, with 20% being a decent starting point. A probability that is too low has no effect, whereas a number that is too high causes the network to under-learn.
When you employ dropout on a larger network, you're more likely to achieve better results because the model has more opportunities to learn independent representations.
