Machine Learning, Artificial Intelligence, Regularization, Dropout Regularization, Overfitting.
learns a more robust set of features that perform equally well with random subsets of the node selected. By randomly dropping connections, the network is able to learn a better-generalized mapping from input to output hence reducing the over-fitting. The dropout ratio needs to be carefully selected