Restricted Boltzmann machine

The Restricted Boltzmann machine (RBM) is a classic example of building blocks of deep probabilistic models that are used for deep learning. The RBM itself is not a deep model but can be used as a building block to form other deep models. In fact, RBMs are undirected probabilistic graphical models that consist of a layer of observed variables and a single layer of hidden variables, which may be used to learn the representation for the input. In this section, we will explain how the RBM can be used to build many deeper models.

Let us consider two examples to see the use case of RBM. RBM primarily operates on a binary version of factor analysis. Let us say we have a restaurant, and want to ask our customer to rate the food on a scale of 0 to 5. In the traditional approach, we will try to explain each food item and customer in terms of the variable's hidden factors. For example, foods such as pasta and lasagne will have a strong association with the Italian factors...