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Deep Belief Networks (DBNs) are probabilistic generative models composed of multiple layers of hidden variables, often used in unsupervised learning for feature extraction and dimensionality reduction tasks. In the context of deep learning algorithms, DBNs can learn complex representations from input data by stacking multiple layers of Restricted Boltzmann Machines (RBMs), where each RBM is trained to reconstruct its inputs using stochastic binary units with undirected connections between visible and hidden layers. After training individual RBMs, the output activations are used as input for higher-layer RBMs until a deep architecture is formed. DBNs have been successfully applied in various domains such as computer vision, natural language processing, and speech recognition, demonstrating their effectiveness in capturing high-level abstractions within unstructured data.