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Modern Dimension Reduction (Elements in Quantitative and Computational Methods for the Social Sciences)

Waggoner, Philip D.

Edit� par Cambridge University Press, 2021

ISBN 10 : 1108986897 ISBN 13 : 9781108986892

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Modern Dimension Reduction

Philip D. Waggoner

Edit� par Cambridge University Press, 2021

ISBN 10 : 1108986897 ISBN 13 : 9781108986892

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Paperback. Etat : Brand New. 75 pages. 9.02x5.98x0.20 inches. In Stock. This item is printed on demand. N� de r�f. du vendeur __1108986897

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Modern Dimension Reduction (Elements in Quantitative and Computational Methods for the Social Sciences)

Philip D. Waggoner

Edit� par Cambridge University Press 2021-07-31, 2021

ISBN 10 : 1108986897 ISBN 13 : 9781108986892

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Modern Dimension Reduction (Paperback or Softback)

Waggoner, Philip D.

Edit� par Cambridge University Press 8/5/2021, 2021

ISBN 10 : 1108986897 ISBN 13 : 9781108986892

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Modern Dimension Reduction

Waggoner, Philip D.

Edit� par Cambridge University Press, 2021

ISBN 10 : 1108986897 ISBN 13 : 9781108986892

Neuf Couverture souple

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dimension reduction offers researchers and scholars the ability to make complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques to efficiently represent the. N� de r�f. du vendeur 483795968

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Modern Dimension Reduction

Philip D. Waggoner

Edit� par Cambridge University Press, 2021

ISBN 10 : 1108986897 ISBN 13 : 9781108986892

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Dimension reduction offers researchers and scholars the ability to make complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. N� de r�f. du vendeur 9781108986892

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Modern Dimension Reduction (Paperback)

Philip D. Waggoner

Edit� par Cambridge University Press, Cambridge, 2021

ISBN 10 : 1108986897 ISBN 13 : 9781108986892

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Paperback. Etat : new. Paperback. Data are not only ubiquitous in society, but are increasingly complex both in size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques along with hundreds of lines of R code, to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Launching from the earliest dimension reduction technique principal components analysis and using real social science data, I introduce and walk readers through application of the following techniques: locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection, self-organizing maps, and deep autoencoders. The result is a well-stocked toolbox of unsupervised algorithms for tackling the complexities of high dimensional data so common in modern society. All code is publicly accessible on Github. Dimension reduction offers researchers and scholars the ability to make complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. N� de r�f. du vendeur 9781108986892

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Modern Dimension Reduction (Paperback)

Philip D. Waggoner

Edit� par Cambridge University Press, Cambridge, 2021

ISBN 10 : 1108986897 ISBN 13 : 9781108986892

Neuf Paperback

Vendeur : AussieBookSeller, Truganina, VIC, Australie

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Paperback. Etat : new. Paperback. Data are not only ubiquitous in society, but are increasingly complex both in size and dimensionality. Dimension reduction offers researchers and scholars the ability to make such complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques along with hundreds of lines of R code, to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Launching from the earliest dimension reduction technique principal components analysis and using real social science data, I introduce and walk readers through application of the following techniques: locally linear embedding, t-distributed stochastic neighbor embedding (t-SNE), uniform manifold approximation and projection, self-organizing maps, and deep autoencoders. The result is a well-stocked toolbox of unsupervised algorithms for tackling the complexities of high dimensional data so common in modern society. All code is publicly accessible on Github. Dimension reduction offers researchers and scholars the ability to make complex, high dimensional data spaces simpler and more manageable. This Element offers readers a suite of modern unsupervised dimension reduction techniques to efficiently represent the original high dimensional data space in a simplified, lower dimensional subspace. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. N� de r�f. du vendeur 9781108986892

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