expectation-maximization

Expectation–maximization for deep learning is a computational approach that integrates the EM algorithm into neural network training to handle incomplete data and latent variable models. It iteratively estimates missing data and optimizes model parameters, improving learning in unsupervised and semi-supervised contexts.