Machine Learning, Deep Learning, Algorithms, Loss Function, Optimization.
parameters (such as the mean or variance ) from sample data such that the probability (likelihood) of obtaining the observed data is maximized. Consider a set of m examples X = {x(1), . . . , x(m)} drawn independently from the true but unknown data generating distribution Pdata(x). Let Pmodel(x; θ)