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Verlag: SIAM - Society for Industrial and Applied Mathematics, 2021
ISBN 10: 1611976367 ISBN 13: 9781611976366
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Verlag: SIAM - Society for Industrial and Applied Mathematics, 2021
ISBN 10: 1611976367 ISBN 13: 9781611976366
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Verlag: Society For Industrial & Applied Mathematics,U.S., 2021
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Sprache: Englisch
Verlag: SIAM - Society for Industrial and Applied Mathematics, 2021
ISBN 10: 1611976367 ISBN 13: 9781611976366
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Verlag: Society for Industrial and Applied Mathematics,U.S., US, 2021
ISBN 10: 1611976367 ISBN 13: 9781611976366
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In den WarenkorbPaperback. Zustand: New. This textbook provides a solid mathematical basis for understanding popular data science algorithms for clustering and classification and shows that an in-depth understanding of the mathematics powering these algorithms gives insight into the underlying data. It presents a step-by-step derivation of these algorithms, outlining their implementation from scratch in a computationally sound way.Mathematics of Data Science: A Computational Approach to Clustering and Classificationproposes different ways of visualizing high-dimensional data to unveil hidden internal structures, andincludes graphical explanations and computed examples using publicly available data sets in nearly every chapter to highlight similarities and differences among the algorithms.
Sprache: Englisch
Verlag: MP-SIA SIAM - Society for Industrial and Applied M, 2021
ISBN 10: 1611976367 ISBN 13: 9781611976366
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Sprache: Englisch
Verlag: Society for Industrial & Applied Mathematics,U.S., 2021
ISBN 10: 1611976367 ISBN 13: 9781611976366
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Verlag: Society for Industrial & Applied Mathematics,U.S., 2021
ISBN 10: 1611976367 ISBN 13: 9781611976366
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Sprache: Englisch
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ISBN 10: 1611976367 ISBN 13: 9781611976366
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ISBN 10: 1611976367 ISBN 13: 9781611976366
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Verlag: SIAM - Society for Industrial and Applied Mathematics, 2021
ISBN 10: 1611976367 ISBN 13: 9781611976366
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Verlag: Society for Industrial and Applied Mathematics,U.S., US, 2021
ISBN 10: 1611976367 ISBN 13: 9781611976366
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In den WarenkorbPaperback. Zustand: New. This textbook provides a solid mathematical basis for understanding popular data science algorithms for clustering and classification and shows that an in-depth understanding of the mathematics powering these algorithms gives insight into the underlying data. It presents a step-by-step derivation of these algorithms, outlining their implementation from scratch in a computationally sound way.Mathematics of Data Science: A Computational Approach to Clustering and Classificationproposes different ways of visualizing high-dimensional data to unveil hidden internal structures, andincludes graphical explanations and computed examples using publicly available data sets in nearly every chapter to highlight similarities and differences among the algorithms.