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Publicado por Springer International Publishing AG, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Idioma: Inglés
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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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Publicado por Springer International Publishing AG, CH, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Idioma: Inglés
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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
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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.
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Publicado por Springer, Berlin|Springer International Publishing|Springer, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
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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 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
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EUR 87,71
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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.