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Deep Belief Networks are generative models composed of multiple layers of hidden variables, often used in unsupervised learning to extract useful features from data. They utilize an undirected graphical model at the bottom layer and a directed acyclic graph for the remaining layers. This structure allows DBNs to capture complex patterns within data by training each layer as a Restricted Boltzmann Machine, which then serves as the input for the next layer's RBM. Once the entire network is trained, it can generate new samples or serve as a powerful feature extractor for classification tasks. In the context of neural network architectures, DBNs are an important alternative to traditional feedforward networks, enabling more efficient learning and representation of high-dimensional data.