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Fundamentals Pattern Recognition Machine by Braga Neto Ulisses (35 results)
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Fundamentals of Pattern Recognition and Machine Learning
Seller: Books From California, Simi Valley, CA, U.S.A.
Seller rating 5 out of 5 stars
hardcover. Condition: Very Good. Cover and edges may have some wear.
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Condition: New. pp. 357.
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Condition: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.
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US$ 54.84
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US$ 71.59
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Fundamentals of Pattern Recognition and Machine Learning
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
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US$ 68.01
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Add to basketHRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Condition: New.
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Fundamentals of Pattern Recognition and Machine Learning
Seller: Ria Christie Collections, Uxbridge, United Kingdom
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Fundamentals of Pattern Recognition and Machine Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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Fundamentals of Pattern Recognition and Machine Learning (Hardcover)
Published by Springer International Publishing AG, Cham, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Language: English
Hardcover. Condition: 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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Fundamentals of Pattern Recognition and Machine Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
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US$ 76.08
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Condition: New. Second Edition 2024 NO-PA16APR2015-KAP.
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Fundamentals of Pattern Recognition and Machine Learning
Published by Springer International Publishing AG, CH, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
US$ 96.16
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Add to basketHardback. Condition: 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.
-
Fundamentals of Pattern Recognition and Machine Learning
Published by Springer International Publishing AG, CH, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Language: English
Hardback. Condition: 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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US$ 98.88
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Fundamentals of Pattern Recognition and Machine Learning
Published by Springer, Berlin|Springer International Publishing|Springer, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Language: English
US$ 78.57
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Add to basketGebunden. Condition: New.
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Fundamentals of Pattern Recognition and Machine Learning (Hardcover)
Published by Springer International Publishing AG, Cham, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Language: English
US$ 89.95
Convert currencyUS$ 49.34 shipping from United Kingdom to U.S.A.Quantity: 1 available
Add to basketHardcover. Condition: 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.
-
Fundamentals of Pattern Recognition and Machine Learning
Published by Springer International Publishing AG, CH, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Language: English
Hardback. Condition: 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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Fundamentals of Pattern Recognition and Machine Learning
Published by Springer-Nature New York Inc, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Language: English
US$ 121.73
Convert currencyUS$ 33.34 shipping from United Kingdom to U.S.A.Quantity: 2 available
Add to basketHardcover. Condition: Brand New. 2nd edition. 400 pages. 9.25x6.10x10.00 inches. In Stock.
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Fundamentals of Pattern Recognition and Machine Learning
Published by Springer International Publishing, Springer Nature Switzerland Aug 2024, 2024
ISBN 10: 3031609492 ISBN 13: 9783031609497
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
US$ 83.11
Convert currencyUS$ 69.61 shipping from Germany to U.S.A.Quantity: 2 available
Add to basketBuch. Condition: 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.
