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In den WarenkorbZustand: New. pp. 357.
Verlag: Springer International Publishing AG, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Sprache: Englisch
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Verlag: Springer, Berlin|Springer International Publishing|Springer, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Sprache: Englisch
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Verlag: Springer International Publishing, Springer Nature Switzerland Aug 2024, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Sprache: Englisch
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In den WarenkorbBuch. Zustand: Neu. Neuware -This book is a concise but thorough introduction to the tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as deep neural networks and Gaussian process regression. The Second Edition is thoroughly revised, featuring a new chapter on the emerging topic of physics-informed machine learning and additional material on deep neural networks.Combining theory and practice, this book is suitable for the graduate or advanced undergraduate level classroom and self-study. It fills the need of a mathematically-rigorous text that is relevant to the practitioner as well, with datasets from applications in bioinformatics and materials informatics used throughout to illustrate the theory. These datasets are available from the book website to be used in end-of-chapter coding assignments based on python and Keras/Tensorflow. All plots in the text were generated using python scripts and jupyter not Elektronisches Buch, which can be downloaded from the book website.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 424 pp. Englisch.
Verlag: Springer International Publishing, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Sprache: Englisch
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In den WarenkorbBuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is a concise but thorough introduction tothe tools commonly used in pattern recognition and machine learning, including classification, dimensionality reduction, regression, and clustering, as well as recent popular topics such as deep neural networks and Gaussian process regression. The Second Edition is thoroughly revised, featuring a new chapter on the emerging topic of physics-informed machine learning and additional material on deep neural networks.Combining theory and practice, this book is suitable for the graduate or advanced undergraduate levelclassroom and self-study. It fills the need of a mathematically-rigorous text that is relevant to the practitioner as well, with datasets from applications in bioinformatics and materials informatics used throughout to illustrate the theory. These datasets are available from the book website to be used in end-of-chapter coding assignments based on python and Keras/Tensorflow. All plots in the text were generated using python scripts and jupyter not Elektronisches Buch, which can be downloaded from the book website.
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In den WarenkorbZustand: New.
Verlag: Springer-Nature New York Inc, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Sprache: Englisch
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In den WarenkorbHardcover. Zustand: Brand New. 2nd edition. 400 pages. 9.25x6.10x10.00 inches. In Stock.