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Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package. Students are introduced to matrices in two preliminary chapters, before progressing to advanced topics such as the nuclear norm, proximal operators and convex optimization. Highlighted applications include low-rank approximation, matrix completion, subspace learning, logistic regression for binary classification, robust PCA, dimensionality reduction and Procrustes problems. Extensively classroom-tested, the book includes over 200 multiple-choice questions suitable for in-class interactive learning or quizzes, as well as homework exercises (with solutions available for instructors). It encourages active learning with engaging 'explore' questions, with answers at the back of each chapter, and Julia code examples to demonstrate how the mathematics is actually used in practice. A suite of computational notebooks offers a hands-on learning experience for students. This is a perfect textbook for upper-level undergraduates and first-year graduate students who have taken a prior course in linear algebra basics.
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Linear Algebra for Data Science, Machine Learning, and Signal Processing
Editore:
Cambridge University Press, 2024
ISBN 10: 1009418149
ISBN 13: 9781009418140
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hardcover. Condizione: Very Good. Cover and edges may have some wear. Codice articolo mon0003660315
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LINEAR ALGEBRA FOR DATA SCIENCE MACHINE
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Linear Algebra for Data Science, Machine Learning, and Signal Processing
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Linear Algebra for Data Science, Machine Learning, and Signal Processing
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Linear Algebra for Data Science, Machine Learning, and Signal Processing
Editore:
Cambridge University Pr. Mai 2024, 2024
ISBN 10: 1009418149
ISBN 13: 9781009418140
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Da: Wegmann1855, Zwiesel, Germania
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Buch. Condizione: Neu. Neuware -Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package. Students are introduced to matrices in two preliminary chapters, before progressing to advanced topics such as the nuclear norm, proximal operators and convex optimization. Highlighted applications include low-rank approximation, matrix completion, subspace learning, logistic regression for binary classification, robust PCA, dimensionality reduction and Procrustes problems. Extensively classroom-tested, the book includes over 200 multiple-choice questions suitable for in-class interactive learning or quizzes, as well as homework exercises (with solutions available for instructors). It encourages active learning with engaging 'explore' questions, with answers at the back of each chapter, and Julia code examples to demonstrate how the mathematics is actually used in practice. A suite of computational not Elektronisches Buch offers a hands-on learning experience for students. This is a perfect textbook for upper-level undergraduates and first-year graduate students who have taken a prior course in linear algebra basics. Codice articolo 9781009418140
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Linear Algebra for Data Science, Machine Learning, and Signal Processing
Editore:
Cambridge University Pr. Mai 2024, 2024
ISBN 10: 1009418149
ISBN 13: 9781009418140
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Da: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, Germania
Valutazione del venditore 5 su 5 stelle
Buch. Condizione: Neu. Neuware -Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package. Students are introduced to matrices in two preliminary chapters, before progressing to advanced topics such as the nuclear norm, proximal operators and convex optimization. Highlighted applications include low-rank approximation, matrix completion, subspace learning, logistic regression for binary classification, robust PCA, dimensionality reduction and Procrustes problems. Extensively classroom-tested, the book includes over 200 multiple-choice questions suitable for in-class interactive learning or quizzes, as well as homework exercises (with solutions available for instructors). It encourages active learning with engaging 'explore' questions, with answers at the back of each chapter, and Julia code examples to demonstrate how the mathematics is actually used in practice. A suite of computational not Elektronisches Buch offers a hands-on learning experience for students. This is a perfect textbook for upper-level undergraduates and first-year graduate students who have taken a prior course in linear algebra basics. 431 pp. Englisch. Codice articolo 9781009418140
Quantità: 1 disponibili
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Linear Algebra for Data Science, Machine Learning, and Signal Processing
Editore:
Cambridge University Pr. Mai 2024, 2024
ISBN 10: 1009418149
ISBN 13: 9781009418140
Nuovo
Rilegato
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
Valutazione del venditore 5 su 5 stelle
Buch. Condizione: Neu. Neuware -Maximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package. Students are introduced to matrices in two preliminary chapters, before progressing to advanced topics such as the nuclear norm, proximal operators and convex optimization. Highlighted applications include low-rank approximation, matrix completion, subspace learning, logistic regression for binary classification, robust PCA, dimensionality reduction and Procrustes problems. Extensively classroom-tested, the book includes over 200 multiple-choice questions suitable for in-class interactive learning or quizzes, as well as homework exercises (with solutions available for instructors). It encourages active learning with engaging 'explore' questions, with answers at the back of each chapter, and Julia code examples to demonstrate how the mathematics is actually used in practice. A suite of computational not Elektronisches Buch offers a hands-on learning experience for students. This is a perfect textbook for upper-level undergraduates and first-year graduate students who have taken a prior course in linear algebra basics. 431 pp. Englisch. Codice articolo 9781009418140
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Linear Algebra for Data Science, Machine Learning, and Signal Processing
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Linear Algebra for Data Science, Machine Learning, and Signal Processing
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Linear Algebra for Data Science, Machine Learning, and Signal Processing
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Condizione: New. 2024. hardcover. . . . . . Codice articolo V9781009418140
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