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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.
[2] [3] It is a framework with wide support for deep learning algorithms. [4] Deeplearning4j includes implementations of the restricted Boltzmann machine, deep belief net, deep autoencoder, stacked denoising autoencoder and recursive neural tensor network, word2vec, doc2vec, and GloVe.
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.
Restricted Boltzmann machines (RBMs) are often used as a building block for multilayer learning architectures. [ 6 ] [ 24 ] An RBM can be represented by an undirected bipartite graph consisting of a group of binary hidden variables , a group of visible variables, and edges connecting the hidden and visible nodes.
Scala, Python No No Yes Yes Yes Yes Caffe: Berkeley Vision and Learning Center 2013 BSD: Yes Linux, macOS, Windows [3] C++: Python, MATLAB, C++: Yes Under development [4] Yes No Yes Yes [5] Yes Yes No ? No [6] Chainer: Preferred Networks 2015 BSD: Yes Linux, macOS: Python: Python: No No Yes No Yes Yes Yes Yes No Yes No [7] Deeplearning4j
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]
1.4 Other types. 2 Applications. ... Autoencoder; Deep learning ... A restricted Boltzmann machine is a bipartite generative model specified over an undirected graph.
The Boltzmann machine can be thought of as a noisy Hopfield network. It is one of the first neural networks to demonstrate learning of latent variables (hidden units). Boltzmann machine learning was at first slow to simulate, but the contrastive divergence algorithm speeds up training for Boltzmann machines and Products of Experts.