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Tensor Networks for Dimensionality Reduction and Large-Scale Optimization: Part 2 Applications and Future Perspectives - Couverture souple
- Éditeurnow publishers Inc
- Date d'édition2017
- ISBN 10 168083276X
- ISBN 13 9781680832761
- ReliureBroché
- Langueanglais
- Nombre de pages256
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Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives (Foundations and Trends(r) in Machine Learning)
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Now Publishers, 2017
ISBN 10 : 168083276X
ISBN 13 : 9781680832761
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Tensor Networks for Dimensionality Reduction and Large-Scale Optimization: Part 2 Applications and Future Perspectives (Foundations and Trends(r) in Machine Learning)
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Tensor Networks for Dimensionality Reduction and Large-scale Optimization
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Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2, Applications and Future Perspectives
Edité par
now publishers Inc, 2017
ISBN 10 : 168083276X
ISBN 13 : 9781680832761
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Tensor Networks for Dimensionality Reduction and Large-scale Optimization
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Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives (Foundations and Trends in Machine Learning)
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Tensor Networks for Dimensionality Reduction and Large-Scale Optimization: Part 2 Applications and Future Perspectives (Foundations and Trends(r) in Machine Learning)
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Tensor Networks for Dimensionality Reduction and Large-scale Optimization : Part 2 Applications and Future Perspectives
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This monograph builds on Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions by discussing tensor network models for super-compressed higher-order representation of data/parameters and cost functions, together with an outline of their applications in machine learning and data analytics. A particular emphasis is on elucidating, through graphical illustrations, that by virtue of the underlying low-rank tensor approximations and sophisticated contractions of core tensors, tensor networks have the ability to perform distributed computations on otherwise prohibitively large volume of data/parameters, thereby alleviating the curse of dimensionality. The usefulness of this concept is illustrated over a number of applied areas, including generalized regression and classification, generalized eigenvalue decomposition and in the optimization of deep neural networks. The monograph focuses on tensor train (TT) and Hierarchical Tucker (HT) decompositions and their extensions, and on demonstrating the ability of tensor networks to provide scalable solutions for a variety of otherwise intractable large-scale optimization problems.Tensor Networks for Dimensionality Reduction and Large-scale Optimization Parts 1 and 2 can be used as stand-alone texts, or together as a comprehensive review of the exciting field of low-rank tensor networks and tensor decompositions.See also: Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions. ISBN 978-1-68083-222-8. N° de réf. du vendeur 9781680832761
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Tensor Networks for Dimensionality Reduction and Large-scale Optimization
Edité par
Now Publishers Inc, 2017
ISBN 10 : 168083276X
ISBN 13 : 9781680832761
Neuf
Couverture souple
Vendeur : moluna, Greven, Allemagne
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. KlappentextrnrnThis monograph builds on Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions by discussing tensor network models for super-compressed higher-order representation of data. N° de réf. du vendeur 448249777
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