This paper introduces a fast, greedy learning algorithm for training Deep Belief Networks (DBNs). The algorithm involves layer-by-layer training of a stack of Restricted Boltzmann Machines (RBMs), where each layer learns to represent features of the data based on the outputs from the previous layer. This unsupervised pre-training followed by fine-tuning using backpropagation significantly improves the training efficiency and performance of deep neural networks, enabling them to learn complex representations from large datasets.