Seller: GreatBookPricesUK, Woodford Green, United Kingdom
£ 84.23
Convert currencyQuantity: Over 20 available
Add to basketCondition: New.
Seller: GreatBookPrices, Columbia, MD, U.S.A.
£ 71.98
Convert currencyQuantity: Over 20 available
Add to basketCondition: New.
Seller: California Books, Miami, FL, U.S.A.
£ 80.30
Convert currencyQuantity: Over 20 available
Add to basketCondition: New.
Seller: California Books, Miami, FL, U.S.A.
£ 80.30
Convert currencyQuantity: Over 20 available
Add to basketCondition: New.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
£ 85.69
Convert currencyQuantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
£ 86.15
Convert currencyQuantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Condition: New.
Seller: GreatBookPrices, Columbia, MD, U.S.A.
£ 78.29
Convert currencyQuantity: 1 available
Add to basketCondition: New.
Seller: GreatBookPrices, Columbia, MD, U.S.A.
£ 83.66
Convert currencyQuantity: 1 available
Add to basketCondition: As New. Unread book in perfect condition.
Seller: GreatBookPrices, Columbia, MD, U.S.A.
£ 83.98
Convert currencyQuantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
Seller: SecondSale, Montgomery, IL, U.S.A.
£ 73.98
Convert currencyQuantity: 1 available
Add to basketCondition: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
£ 97.63
Convert currencyQuantity: Over 20 available
Add to basketCondition: New. In.
Published by now publishers Inc, US, 2016
ISBN 10: 1680832220 ISBN 13: 9781680832228
Language: English
Seller: Rarewaves USA United, OSWEGO, IL, U.S.A.
£ 99
Convert currencyQuantity: Over 20 available
Add to basketPaperback. Condition: New. Modern applications in engineering and data science are increasingly based on multidimensional data of exceedingly high volume, variety, and structural richness. However, standard machine learning and data mining algorithms typically scale exponentially with data volume and complexity of cross-modal couplings - the so called curse of dimensionality - which is prohibitive to the analysis of such large-scale, multi-modal and multi-relational datasets. Given that such data are often conveniently represented as multiway arrays or tensors, it is therefore timely and valuable for the multidisciplinary machine learning and data analytic communities to review tensor decompositions and tensor networks as emerging tools for dimensionality reduction and large scale optimization.This monograph provides a systematic and example-rich guide to the basic properties and applications of tensor network methodologies, and demonstrates their promise as a tool for the analysis of extreme-scale multidimensional data. It demonstrates the ability of tensor networks to provide linearly or even super-linearly, scalable solutions.The low-rank tensor network framework of analysis presented in this monograph is intended to both help demystify tensor decompositions for educational purposes and further empower practitioners with enhanced intuition and freedom in algorithmic design for the manifold applications. In addition, the material may be useful in lecture courses on large-scale machine learning and big data analytics, or indeed, as interesting reading for the intellectually curious and generally knowledgeable reader.
Published by now publishers Inc, US, 2017
ISBN 10: 168083276X ISBN 13: 9781680832761
Language: English
Seller: Rarewaves USA United, OSWEGO, IL, U.S.A.
£ 99.72
Convert currencyQuantity: Over 20 available
Add to basketPaperback. Condition: New. 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 largescale 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.
Seller: Revaluation Books, Exeter, United Kingdom
Paperback. Condition: Brand New. 262 pages. 9.21x6.14x0.63 inches. In Stock.
Seller: Revaluation Books, Exeter, United Kingdom
Paperback. Condition: Brand New. 196 pages. 9.21x6.14x0.42 inches. In Stock.
Published by now publishers Inc, US, 2016
ISBN 10: 1680832220 ISBN 13: 9781680832228
Language: English
Seller: Rarewaves.com UK, London, United Kingdom
£ 110.20
Convert currencyQuantity: Over 20 available
Add to basketPaperback. Condition: New. Modern applications in engineering and data science are increasingly based on multidimensional data of exceedingly high volume, variety, and structural richness. However, standard machine learning and data mining algorithms typically scale exponentially with data volume and complexity of cross-modal couplings - the so called curse of dimensionality - which is prohibitive to the analysis of such large-scale, multi-modal and multi-relational datasets. Given that such data are often conveniently represented as multiway arrays or tensors, it is therefore timely and valuable for the multidisciplinary machine learning and data analytic communities to review tensor decompositions and tensor networks as emerging tools for dimensionality reduction and large scale optimization.This monograph provides a systematic and example-rich guide to the basic properties and applications of tensor network methodologies, and demonstrates their promise as a tool for the analysis of extreme-scale multidimensional data. It demonstrates the ability of tensor networks to provide linearly or even super-linearly, scalable solutions.The low-rank tensor network framework of analysis presented in this monograph is intended to both help demystify tensor decompositions for educational purposes and further empower practitioners with enhanced intuition and freedom in algorithmic design for the manifold applications. In addition, the material may be useful in lecture courses on large-scale machine learning and big data analytics, or indeed, as interesting reading for the intellectually curious and generally knowledgeable reader.
Seller: Ria Christie Collections, Uxbridge, United Kingdom
£ 110.50
Convert currencyQuantity: Over 20 available
Add to basketCondition: New. In.
Published by now publishers Inc, US, 2017
ISBN 10: 168083276X ISBN 13: 9781680832761
Language: English
Seller: Rarewaves.com UK, London, United Kingdom
£ 111.94
Convert currencyQuantity: Over 20 available
Add to basketPaperback. Condition: New. 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 largescale 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.
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
Published by Now Publishers Inc Dez 2016, 2016
ISBN 10: 1680832220 ISBN 13: 9781680832228
Language: English
Seller: AHA-BUCH GmbH, Einbeck, Germany
£ 110.67
Convert currencyQuantity: 1 available
Add to basketTaschenbuch. Condition: Neu. Neuware - Modern applications in engineering and data science are increasingly based on multidimensional data of exceedingly high volume, variety, and structural richness. However, standard machine learning and data mining algorithms typically scale exponentially with data volume and complexity of cross-modal couplings - the so called curse of dimensionality - which is prohibitive to the analysis of such large-scale, multi-modal and multi-relational datasets. Given that such data are often conveniently represented as multiway arrays or tensors, it is therefore timely and valuable for the multidisciplinary machine learning and data analytic communities to review tensor decompositions and tensor networks as emerging tools for dimensionality reduction and large scale optimization.This monograph provides a systematic and example-rich guide to the basic properties and applications of tensor network methodologies, and demonstrates their promise as a tool for the analysis of extreme-scale multidimensional data. It demonstrates the ability of tensor networks to provide linearly or even super-linearly, scalable solutions.The low-rank tensor network framework of analysis presented in this monograph is intended to both help demystify tensor decompositions for educational purposes and further empower practitioners with enhanced intuition and freedom in algorithmic design for the manifold applications. In addition, the material may be useful in lecture courses on large-scale machine learning and big data analytics, or indeed, as interesting reading for the intellectually curious and generally knowledgeable reader.
Seller: Lucky's Textbooks, Dallas, TX, U.S.A.
£ 70.97
Convert currencyQuantity: Over 20 available
Add to basketCondition: New.
Seller: Books Puddle, New York, NY, U.S.A.
£ 126.80
Convert currencyQuantity: 4 available
Add to basketCondition: New. pp. 196.
Seller: Books Puddle, New York, NY, U.S.A.
£ 129.62
Convert currencyQuantity: 4 available
Add to basketCondition: New. pp. 262.
Published by now publishers Inc, US, 2016
ISBN 10: 1680832220 ISBN 13: 9781680832228
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
£ 125.08
Convert currencyQuantity: Over 20 available
Add to basketPaperback. Condition: New. Modern applications in engineering and data science are increasingly based on multidimensional data of exceedingly high volume, variety, and structural richness. However, standard machine learning and data mining algorithms typically scale exponentially with data volume and complexity of cross-modal couplings - the so called curse of dimensionality - which is prohibitive to the analysis of such large-scale, multi-modal and multi-relational datasets. Given that such data are often conveniently represented as multiway arrays or tensors, it is therefore timely and valuable for the multidisciplinary machine learning and data analytic communities to review tensor decompositions and tensor networks as emerging tools for dimensionality reduction and large scale optimization.This monograph provides a systematic and example-rich guide to the basic properties and applications of tensor network methodologies, and demonstrates their promise as a tool for the analysis of extreme-scale multidimensional data. It demonstrates the ability of tensor networks to provide linearly or even super-linearly, scalable solutions.The low-rank tensor network framework of analysis presented in this monograph is intended to both help demystify tensor decompositions for educational purposes and further empower practitioners with enhanced intuition and freedom in algorithmic design for the manifold applications. In addition, the material may be useful in lecture courses on large-scale machine learning and big data analytics, or indeed, as interesting reading for the intellectually curious and generally knowledgeable reader.
Published by now publishers Inc, US, 2017
ISBN 10: 168083276X ISBN 13: 9781680832761
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
£ 127.05
Convert currencyQuantity: Over 20 available
Add to basketPaperback. Condition: New. 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 largescale 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.
Published by now publishers Inc, US, 2016
ISBN 10: 1680832220 ISBN 13: 9781680832228
Language: English
Seller: Rarewaves USA, OSWEGO, IL, U.S.A.
£ 100.06
Convert currencyQuantity: Over 20 available
Add to basketPaperback. Condition: New. Modern applications in engineering and data science are increasingly based on multidimensional data of exceedingly high volume, variety, and structural richness. However, standard machine learning and data mining algorithms typically scale exponentially with data volume and complexity of cross-modal couplings - the so called curse of dimensionality - which is prohibitive to the analysis of such large-scale, multi-modal and multi-relational datasets. Given that such data are often conveniently represented as multiway arrays or tensors, it is therefore timely and valuable for the multidisciplinary machine learning and data analytic communities to review tensor decompositions and tensor networks as emerging tools for dimensionality reduction and large scale optimization.This monograph provides a systematic and example-rich guide to the basic properties and applications of tensor network methodologies, and demonstrates their promise as a tool for the analysis of extreme-scale multidimensional data. It demonstrates the ability of tensor networks to provide linearly or even super-linearly, scalable solutions.The low-rank tensor network framework of analysis presented in this monograph is intended to both help demystify tensor decompositions for educational purposes and further empower practitioners with enhanced intuition and freedom in algorithmic design for the manifold applications. In addition, the material may be useful in lecture courses on large-scale machine learning and big data analytics, or indeed, as interesting reading for the intellectually curious and generally knowledgeable reader.
Published by now publishers Inc, US, 2017
ISBN 10: 168083276X ISBN 13: 9781680832761
Language: English
Seller: Rarewaves USA, OSWEGO, IL, U.S.A.
£ 100.79
Convert currencyQuantity: Over 20 available
Add to basketPaperback. Condition: New. 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 largescale 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.
Seller: Mispah books, Redhill, SURRE, United Kingdom
paperback. Condition: New. New. book.
Seller: PBShop.store US, Wood Dale, IL, U.S.A.
£ 84.24
Convert currencyQuantity: 15 available
Add to basketPAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.