Articoli correlati a Modern Dimension Reduction
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In the big data age, this Element offers many modern methods and opensource code for learning from high dimensional data.
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Modern Dimension Reduction
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Cambridge University Press, 2021
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Modern Dimension Reduction
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Modern Dimension Reduction (Elements in Quantitative and Computational Methods for the Social Sciences)
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Modern Dimension Reduction
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Condizione: 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. Codice articolo 483795968
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Modern Dimension Reduction (Elements in Quantitative and Computational Methods for the Social Sciences)
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Cambridge University Press 2021-07-31, 2021
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ISBN 13: 9781108986892
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Modern Dimension Reduction
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Cambridge University Press, 2021
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ISBN 13: 9781108986892
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Taschenbuch. Condizione: 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. Codice articolo 9781108986892
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Modern Dimension Reduction (Paperback)
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Cambridge University Press, Cambridge, 2021
ISBN 10: 1108986897
ISBN 13: 9781108986892
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Paperback. Condizione: 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. Codice articolo 9781108986892
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Modern Dimension Reduction (Paperback)
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Cambridge University Press, Cambridge, 2021
ISBN 10: 1108986897
ISBN 13: 9781108986892
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Paperback. Condizione: 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. Codice articolo 9781108986892
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Modern Dimension Reduction
Editore:
Cambridge University Press, 2021
ISBN 10: 1108986897
ISBN 13: 9781108986892
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Taschenbuch. Condizione: Neu. Modern Dimension Reduction | Philip D. Waggoner | Taschenbuch | Kartoniert / Broschiert | Englisch | 2021 | Cambridge University Press | EAN 9781108986892 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. Codice articolo 120235863
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