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Diagram of a restricted Boltzmann machine with three visible units and four hidden units (no bias units) A restricted Boltzmann machine (RBM) (also called a restricted Sherrington–Kirkpatrick model with external field or restricted stochastic Ising–Lenz–Little model) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.
The code is hosted on GitHub. [13] A support forum is maintained on Gitter. [14] The framework is composable, meaning shallow neural nets such as restricted Boltzmann machines, convolutional nets, autoencoders, and recurrent nets can be added to one another to create deep nets of varying types.
This is not a restricted Boltzmann machine. A Boltzmann machine (also called Sherrington–Kirkpatrick model with external field or stochastic Ising model), named after Ludwig Boltzmann is a spin-glass model with an external field, i.e., a Sherrington–Kirkpatrick model, [1] that is a stochastic Ising model.
It uses a restricted Boltzmann machine to model each new layer of higher level features. Each new layer guarantees an increase on the lower-bound of the log likelihood of the data, thus improving the model, if trained properly.
In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer.
In computer science, a convolutional deep belief network (CDBN) is a type of deep artificial neural network composed of multiple layers of convolutional restricted Boltzmann machines stacked together. [1]
A restricted Boltzmann machine is a bipartite generative model specified over an undirected graph. Applications ... decoding of low-density parity-check codes, ...
Deutsch: Restricted Boltzmann Machine (RBM) The zu lernenden Parameter sind rot eingefärbt. Die "visible units" v sind durch ungerichtete Kanten mit den versteckten Einheiten (hidden units) verbunden.