Restricted Boltzmann Machine (RBM)

Restricted Boltzmann Machine is used to detect patterns in data, in an unsupervised way. If you haven't read the previous posts yet, you can read them by clicking the below links. RBMs are self-learning shallow neural networks that learn to reassemble data. They're significant models because they can extract meaningful features from a given input without having to identify them. Let's start with the fact that we have access to a matrix of viewer ratings for a specific number of Netflix movies, where each row represents a movie and each column represents a user's rating.