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  2. Restricted Boltzmann machine - Wikipedia

    en.wikipedia.org/wiki/Restricted_Boltzmann_machine

    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.

  3. Deeplearning4j - Wikipedia

    en.wikipedia.org/wiki/Deeplearning4j

    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. These algorithms all include distributed parallel versions that integrate with Apache Hadoop and Spark. [5]

  4. Deep belief network - Wikipedia

    en.wikipedia.org/wiki/Deep_belief_network

    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.

  5. Types of artificial neural networks - Wikipedia

    en.wikipedia.org/wiki/Types_of_artificial_neural...

    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.

  6. Autoencoder - Wikipedia

    en.wikipedia.org/wiki/Autoencoder

    An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation.

  7. Feature learning - Wikipedia

    en.wikipedia.org/wiki/Feature_learning

    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.

  8. Nonlinear dimensionality reduction - Wikipedia

    en.wikipedia.org/wiki/Nonlinear_dimensionality...

    An autoencoder is a feed-forward neural network ... possible through the use of restricted Boltzmann machines and stacked ... implemented in Python (code ...

  9. Category:Supervised learning - Wikipedia

    en.wikipedia.org/wiki/Category:Supervised_learning

    Restricted Boltzmann machine; V. Variational autoencoder This page was last edited on 10 October 2019, at 06:27 (UTC). Text is available under the Creative ...