Lingua: Inglese
Editore: Gulf Professional Publishing, 2019
ISBN 10: 0128177365 ISBN 13: 9780128177365
Da: Gardner's Used Books, Inc., Tulsa, OK, U.S.A.
Paperback. Condizione: Acceptable. Softcover. Excellent condition but does contain a minimal amount of highlighting (mostly in chapter 1-all text is legible). Intact, complete. Tulsa's largest used bookstore. Located on South Mingo Road since 1991. No-hassle return policy if not completely satisfied.
Da: Majestic Books, Hounslow, Regno Unito
EUR 130,02
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Lingua: Inglese
Editore: Gulf Professional Publishing, 2019
ISBN 10: 0128177365 ISBN 13: 9780128177365
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 143,74
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Da: Books Puddle, New York, NY, U.S.A.
Condizione: New.
Lingua: Inglese
Editore: Elsevier Science & Technology, 2019
ISBN 10: 0128177365 ISBN 13: 9780128177365
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 139,10
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Aggiungi al carrelloPaperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 147,58
Quantità: 3 disponibili
Aggiungi al carrelloCondizione: New.
Lingua: Inglese
Editore: Gulf Professional Publishing, 2019
ISBN 10: 0128177365 ISBN 13: 9780128177365
Da: GreatBookPrices, Columbia, MD, U.S.A.
EUR 165,93
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Gulf Professional Publishing, 2019
ISBN 10: 0128177365 ISBN 13: 9780128177365
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 156,69
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Aggiungi al carrelloCondizione: New. In.
Lingua: Inglese
Editore: Gulf Professional Publishing, 2019
ISBN 10: 0128177365 ISBN 13: 9780128177365
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 156,68
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Lingua: Inglese
Editore: Gulf Professional Publishing, 2019
ISBN 10: 0128177365 ISBN 13: 9780128177365
Da: GreatBookPricesUK, Woodford Green, Regno Unito
EUR 172,56
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: Gulf Professional Publishing, 2019
ISBN 10: 0128177365 ISBN 13: 9780128177365
Da: preigu, Osnabrück, Germania
EUR 116,85
Quantità: 5 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. Machine Learning for Subsurface Characterization | Siddharth Misra (u. a.) | Taschenbuch | Einband - fest (Hardcover) | Englisch | 2019 | Gulf Professional Publishing | EAN 9780128177365 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
EUR 161,53
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Aggiungi al carrelloKartoniert / Broschiert. Condizione: New. Learn from 13 practical case studies using field, laboratory, and simulation data Become knowledgeable with data science and analytics terminology relevant to subsurface characterization Learn frameworks, concepts, and methods imp.
Lingua: Inglese
Editore: Gulf Professional Publishing, 2019
ISBN 10: 0128177365 ISBN 13: 9780128177365
Da: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 111,23
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Aggiungi al carrelloCondizione: new. Questo è un articolo print on demand.
Lingua: Inglese
Editore: Gulf Professional Publishing, 2019
ISBN 10: 0128177365 ISBN 13: 9780128177365
Da: Revaluation Books, Exeter, Regno Unito
EUR 127,39
Quantità: 2 disponibili
Aggiungi al carrelloPaperback. Condizione: Brand New. 230 pages. 9.00x6.00x0.51 inches. In Stock. This item is printed on demand.
Lingua: Inglese
Editore: Elsevier Science & Technology, Gulf Professional Publishing, 2019
ISBN 10: 0128177365 ISBN 13: 9780128177365
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 123,00
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the 'big data.' Deep learning (DL) is a subset of machine learning that processes 'big data' to construct numerous layers of abstraction to accomplish the learning task. DL methods do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface. Englisch.
Lingua: Inglese
Editore: Elsevier Science & Technology, Gulf Professional Publishing, 2019
ISBN 10: 0128177365 ISBN 13: 9780128177365
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 137,13
Quantità: 2 disponibili
Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the 'big data.' Deep learning (DL) is a subset of machine learning that processes 'big data' to construct numerous layers of abstraction to accomplish the learning task. DL methods do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface.