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ISBN 10: 1108489737 ISBN 13: 9781108489737
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Published by Cambridge University Press, 2022
ISBN 10: 1108489737 ISBN 13: 9781108489737
Language: English
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Add to basketCondition: good. A well-loved companion. Corners and cover might show a little wear, and you could find some notes or highlights. The dust jacket might be MIA, it might have been a library book and extras arenât guaranteedâ"but the storyâs all there!
Published by Cambridge University Press, 2022
ISBN 10: 1108489737 ISBN 13: 9781108489737
Language: English
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Published by Cambridge University Press, 2022
ISBN 10: 1108489737 ISBN 13: 9781108489737
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Published by Cambridge University Press, 2022
ISBN 10: 1108489737 ISBN 13: 9781108489737
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Published by Cambridge University Press, 2022
ISBN 10: 1108489737 ISBN 13: 9781108489737
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Published by Cambridge University Press, 2022
ISBN 10: 1108489737 ISBN 13: 9781108489737
Language: English
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ISBN 10: 1108489737 ISBN 13: 9781108489737
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Published by Cambridge University Press, 2022
ISBN 10: 1108489737 ISBN 13: 9781108489737
Language: English
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ISBN 10: 1108489737 ISBN 13: 9781108489737
Language: English
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Published by Cambridge University Press, 2022
ISBN 10: 1108489737 ISBN 13: 9781108489737
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Published by Cambridge University Press, 2022
ISBN 10: 1108489737 ISBN 13: 9781108489737
Language: English
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Published by Cambridge University Press Jan 2022, 2022
ISBN 10: 1108489737 ISBN 13: 9781108489737
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Add to basketBuch. Condition: Neu. Neuware - Connects fundamental mathematical theory with real-world problems, through efficient and scalable optimization algorithms.
Published by Cambridge University Press, Cambridge, 2022
ISBN 10: 1108489737 ISBN 13: 9781108489737
Language: English
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Hardcover. Condition: new. Hardcover. Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candes. A systematic introduction to the theory, algorithms, and applications of key mathematical models for data science. Covering applications including imaging, communication, and face recognition, with online code, it is ideal for senior/graduate students in computer science, data science, and electrical engineering. With foreword by Emmanuel Candes. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
Published by Cambridge University Press, 2022
ISBN 10: 1108489737 ISBN 13: 9781108489737
Language: English
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Add to basketCondition: New. A systematic introduction to the theory, algorithms, and applications of key mathematical models for data science. Covering applications including imaging, communication, and face recognition, with online code, it is ideal for senior/graduate students in co.
Published by Cambridge University Press, 2022
ISBN 10: 1108489737 ISBN 13: 9781108489737
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Hardcover. Condition: Brand New. 650 pages. 9.96x6.93x1.46 inches. In Stock.
Published by Cambridge University Press, Cambridge, 2022
ISBN 10: 1108489737 ISBN 13: 9781108489737
Language: English
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Add to basketHardcover. Condition: new. Hardcover. Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional structures, and high-dimensional data. Foreword by Emmanuel Candes. A systematic introduction to the theory, algorithms, and applications of key mathematical models for data science. Covering applications including imaging, communication, and face recognition, with online code, it is ideal for senior/graduate students in computer science, data science, and electrical engineering. With foreword by Emmanuel Candes. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Seller: Revaluation Books, Exeter, United Kingdom
Hardcover. Condition: Brand New. 650 pages. 9.96x6.93x1.46 inches. In Stock. This item is printed on demand.