Machine Learning, Deep Learning, Artificial Intelligence, Regularization, Deep Learning Book.
by reducing the mean square error. Now, suppose a zero mean unit variance Gaussian random noise, ϵ, is added to the weights. We till want to learn the appropriate mapping through reducing the mean square. Minimizing the loss after adding noise to the weights is equivalent to adding another