Deep Belief Networks

Deep Belief Networks (DBNs) are a type of deep learning model composed of multiple layers of stochastic, latent variables. They are typically formed by stacking Restricted Boltzmann Machines (RBMs) or similar architectures, allowing them to learn complex representations of data through unsupervised learning. DBNs are characterized by their ability to perform feature extraction and dimensionality reduction, making them useful for tasks such as image recognition, speech recognition, and more. Common use cases include pre-training deep neural networks, initializing weights for supervised learning, and exploring generative models.