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Editore: Springer International Publishing AG, CH, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
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ISBN 10: 3031609492 ISBN 13: 9783031609497
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Hardcover. Condizione: new. Hardcover. 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 notebooks, which can be downloaded from the book website. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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ISBN 10: 3031609492 ISBN 13: 9783031609497
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ISBN 10: 3031609492 ISBN 13: 9783031609497
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Aggiungi al carrelloBuch. Condizione: 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.
Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
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Aggiungi al carrelloHardcover. Condizione: new. Hardcover. 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 notebooks, which can be downloaded from the book website. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Lingua: Inglese
Editore: Springer-Nature New York Inc, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
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Editore: Springer, Berlin, Springer International Publishing, Springer, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 400 pp. Englisch.
Lingua: Inglese
Editore: Springer, Birkhäuser Aug 2024, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
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Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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 KG, Sachsenplatz 4-6, 1201 Wien 424 pp. Englisch.
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Aggiungi al carrelloBuch. Condizione: Neu. Fundamentals of Pattern Recognition and Machine Learning | Ulisses Braga-Neto | Buch | xxi | Englisch | 2024 | Springer | EAN 9783031609497 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand.