Machine Learning, Deep Learning, Artificial Intelligence, Regularization, Deep Learning Book.
considered to be uncertain, with the uncertainty being modelled by a probability distribution. It is also interpreted as a more traditional form of regularization by ensuring stability in learning. For e.g. in the linear regression case, we want to learn the mapping y(x) for each feature vector x,