In this chapter, we explored one of the most interesting research sites on modeling with neural networks. First we saw an introduction to unsupervised learning algorithms. Unsupervised learning is a machine learning technique that, starting from a series of inputs (system experience), will be able to reclassify and organize on the basis of common characteristics to try to make predictions on subsequent inputs. Unlike supervised learning, only unlabeled examples are provided to the learner during the learning process, as the classes are not known a priori but must be learned automatically.
So, we analyzed different types of generative models. A Boltzmann machine is a probabilistic graphic model that can be interpreted as a stochastic neural network. In practice, a Boltzmann machine is a model (including a certain number of parameters) that, when applied to a data distribution...
