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Añadir al carritoCondición: New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Añadir al carritoCondición: New. pp. 357.
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Publicado por Springer International Publishing AG, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Idioma: Inglés
Librería: PBShop.store UK, Fairford, GLOS, Reino Unido
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Añadir al carritohardcover. Condición: New.
Publicado por Springer International Publishing AG, Cham, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Idioma: Inglés
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Añadir al carritoHardcover. Condición: 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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Librería: Books Puddle, New York, NY, Estados Unidos de America
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Añadir al carritoCondición: New. Second Edition 2024 NO-PA16APR2015-KAP.
Publicado por Springer International Publishing AG, CH, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Idioma: Inglés
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
EUR 85,28
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Añadir al carritoHardback. Condición: New. Second Edition 2024. 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.
Publicado por Springer International Publishing AG, CH, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Idioma: Inglés
Librería: Rarewaves USA, OSWEGO, IL, Estados Unidos de America
EUR 85,99
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Añadir al carritoHardback. Condición: New. Second Edition 2024. 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.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Publicado por Springer, Berlin|Springer International Publishing|Springer, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Idioma: Inglés
Librería: moluna, Greven, Alemania
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Añadir al carritoGebunden. Condición: New.
Publicado por Springer International Publishing AG, Cham, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Idioma: Inglés
Librería: CitiRetail, Stevenage, Reino Unido
EUR 77,44
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Añadir al carritoHardcover. Condición: 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 UK warehouse or from our Australian or US warehouses, depending on stock availability.
Publicado por Springer International Publishing AG, CH, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Idioma: Inglés
Librería: Rarewaves USA United, OSWEGO, IL, Estados Unidos de America
EUR 88,16
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Añadir al carritoHardback. Condición: New. Second Edition 2024. 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.
Publicado por Springer International Publishing, Springer Nature Switzerland Aug 2024, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Idioma: Inglés
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
EUR 69,54
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Añadir al carritoBuch. Condición: 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.
Publicado por Springer-Nature New York Inc, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Idioma: Inglés
Librería: Revaluation Books, Exeter, Reino Unido
EUR 104,12
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Añadir al carritoHardcover. Condición: Brand New. 2nd edition. 400 pages. 9.25x6.10x10.00 inches. In Stock.