10 relations: Artificial neural network, Autoencoder, Boltzmann machine, Generative model, Hermann von Helmholtz, Hopfield network, Representations, Restricted Boltzmann machine, Unsupervised learning, Wake-sleep algorithm.
Artificial neural network
Artificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains.
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Autoencoder
An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner.
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Boltzmann machine
A Boltzmann machine (also called stochastic Hopfield network with hidden units) is a type of stochastic recurrent neural network (and Markov random field).
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Generative model
In statistical classification, including machine learning, two main approaches are called the generative approach and the discriminative approach.
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Hermann von Helmholtz
Hermann Ludwig Ferdinand von Helmholtz (August 31, 1821 – September 8, 1894) was a German physician and physicist who made significant contributions in several scientific fields.
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Hopfield network
A Hopfield network is a form of recurrent artificial neural network popularized by John Hopfield in 1982, but described earlier by Little in 1974.
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Representations
Representations is an interdisciplinary journal in the humanities published quarterly by the University of California Press.
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Restricted Boltzmann machine
A restricted Boltzmann machine (RBM) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.
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Unsupervised learning
Unsupervised machine learning is the machine learning task of inferring a function that describes the structure of "unlabeled" data (i.e. data that has not been classified or categorized).
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Wake-sleep algorithm
The wake-sleep algorithm is an unsupervised learning algorithm for a stochastic multilayer neural network.
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