A Profond Réseau de croyance (DBN) est un génératif modèle graphique that consists of multiple layers of hidden units, with connections between the layers but not within them. DBNs are composed of multiple Machines de Boltzmann restreintes (RBMs), which learn to represent the input data through apprentissage non supervisé.
Chaque layer of the DBN captures different levels of abstraction from the input data, allowing it to learn hierarchical representations. The learning process typically involves two main phases: pre-training and fine-tuning. In the pre-training phase, each RBM is trained one at a time in a greedy manner. Once all layers are trained, the network undergoes a fine-tuning phase where apprentissage supervisé techniques, such as backpropagation, are applied to adjust the weights and minimize the error on a specific task.
DBNs are particularly useful in applications such as image recognition, speech recognition, and traitement du langage naturel, where complex patterns and structures in the data need to be captured. By stacking multiple layers, a DBN can model intricate relationships and dependencies in the data, leading to improved performance on various tasks compared to shallower networks.
Despite their effectiveness, DBNs have largely been surpassed by other deep learning architectures, such as Réseaux de neurones convolutifs (CNNs) and Réseaux de Neurones Récurrents (RNN), qui sont plus couramment utilisés pour des tâches spécifiques aujourd'hui.