darch

darch: Package for deep architectures and Restricted-Bolzmann-Machines. The darch package is build on the basis of the code from G. E. Hinton and R. R. Salakhutdinov (available under Matlab Code for deep belief nets : last visit: 01.08.2013). This package is for generating neural networks with many layers (deep architectures) and train them with the method introduced by the publications ”A fast learning algorithm for deep belief nets” (G. E. Hinton, S. Osindero, Y. W. Teh) and ”Reducing the dimensionality of data with neural networks” (G. E. Hinton, R. R. Salakhutdinov). This method includes a pre training with the contrastive divergence method publishing by G.E Hinton (2002) and a fine tuning with common known training algorithms like backpropagation or conjugate gradient.


References in zbMATH (referenced in 247 articles )

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  10. Osman, Yousuf Babiker M.; Li, Wei: Soft sensor modeling of key effluent parameters in wastewater treatment process based on SAE-NN (2020)
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  12. Pradhan, Anshuman; Mukerji, Tapan: Seismic Bayesian evidential learning: estimation and uncertainty quantification of sub-resolution reservoir properties (2020)
  13. Puligilla, Shivakanth Chary; Jayaraman, Balaji: Assessment of end-to-end and sequential data-driven learning for non-intrusive modeling of fluid flows (2020)
  14. Ruehle, Fabian: Data science applications to string theory (2020)
  15. Shang, Yifan; Mao, Xiaobo; Zhao, Yuping; Li, Nan; Wang, Yang: Classification of tongue color based on convolution neural network (2020)
  16. Sheikh, Mansoor; Coolen, A. C. C.: Accurate Bayesian data classification without hyperparameter cross-validation (2020)
  17. Stanko, Ivana: The architectures of Geoffrey Hinton (2020)
  18. Tao, Hongfeng; Wang, Peng; Chen, Yiyang; Stojanovic, Vladimir; Yang, Huizhong: An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks (2020)
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  20. van Engelen, Jesper E.; Hoos, Holger H.: A survey on semi-supervised learning (2020)

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