In machine learning, there is a field that is often discussed when talking about deep learning (DL), called deep belief networks (DBNs) (Sutskever, I., and Hinton, G. E. (2008)). Generally speaking, this term is used also for a type of machine learning model based on graphs, such as the well-known Restricted Boltzmann Machine. However, DBNs are usually regarded as part of the DL family, with deep autoencoders as one of the most notable members of that family.
Deep autoencoders are considered DBNs in the sense that there are latent variables that are only visible to single layers in the forward direction. These layers are usually many in number compared to autoencoders with a single pair of layers. One of the main tenets of DL and DBNs in general is that during the learning process, there is different knowledge represented across different sets of layers. This knowledge representation is learned by feature learning without a bias toward a specific class...