Comparer des méthodes

	Réseau de croyance profond (DBN)	Perceptron multicouche (MLP)
Domaine	Apprentissage profond	Apprentissage profond
Famille	Machine learning	Machine learning
Année d'origine	2006	1986
Auteur d'origine	Geoffrey Hinton, Simon Osindero & Yee-Whye Teh	Rumelhart, D. E.; Hinton, G. E.; Williams, R. J.
Type	Generative probabilistic model	Supervised feedforward neural network
Source fondatrice	Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554.	Rumelhart, D. E., Hinton, G. E. & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533–536.
Alias	DBN, Deep Generative Network, Stacked RBM Network, Derin İnanç Ağı	MLP, feedforward neural network, fully connected neural network, vanilla neural network
Apparentées	3	4
Résumé	A Deep Belief Network is a generative probabilistic model composed of multiple layers of stochastic, latent variables. Introduced by Hinton, Osindero, and Teh in 2006, DBNs were among the first deep architectures to be trained efficiently. Each pair of adjacent layers forms a Restricted Boltzmann Machine, and the network is trained greedily, one layer at a time, before optional supervised fine-tuning. DBNs revived interest in deep learning and demonstrated that hierarchical feature learning from raw data is tractable.	A Multilayer Perceptron is a classic fully connected feedforward neural network trained with the backpropagation algorithm, as formalised by Rumelhart, Hinton & Williams in their landmark 1986 Nature paper. Composed of an input layer, one or more hidden layers of neurons, and an output layer, the MLP learns nonlinear mappings from input features to target outputs and serves as the foundational building block of modern deep learning.