Idioma: Inglés
Publicado por Morgan Kaufmann 2015-01-21, 2015
ISBN 10: 0124172954 ISBN 13: 9780124172951
Librería: Chiron Media, Wallingford, Reino Unido
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Añadir al carritoPaperback. Condición: Brand New. 1st edition. 406 pages. 9.75x7.50x1.00 inches. In Stock.
Librería: Books Puddle, New York, NY, Estados Unidos de America
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Añadir al carritoCondición: New. pp. 406.
Idioma: Inglés
Publicado por Elsevier Science & Technology, 2014
ISBN 10: 0124172954 ISBN 13: 9780124172951
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
EUR 80,43
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Añadir al carritoPaperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.
Librería: Biblios, Frankfurt am main, HESSE, Alemania
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Añadir al carritoCondición: New. pp. 406.
Publicado por Morgan Kaufmann, 2015
Librería: My November Guest Books, Beaver falls, PA, Estados Unidos de America
Original o primera edición
EUR 60,78
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Añadir al carritoSoft cover. Condición: Near Fine. 1st Edition. Near fine softback copyright 2015; 378 pages B-40.
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Añadir al carritoTaschenbuch. Condición: Neu. Sharing Data and Models in Software Engineering | Tim Menzies (u. a.) | Taschenbuch | Englisch | 2014 | Elsevier Science | EAN 9780124172951 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Librería: Brook Bookstore On Demand, Napoli, NA, Italia
EUR 65,09
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Añadir al carritoCondición: new. Questo è un articolo print on demand.
Idioma: Inglés
Publicado por Elsevier Science Dez 2014, 2014
ISBN 10: 0124172954 ISBN 13: 9780124172951
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
EUR 150,00
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Añadir al carritoTaschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Data Science for Software Engineering: Sharing Data and Models presents guidance and procedures for reusing data and models between projects to produce results that are useful and relevant. Starting with a background section of practical lessons and warnings for beginner data scientists for software engineering, this edited volume proceeds to identify critical questions of contemporary software engineering related to data and models. Learn how to adapt data from other organizations to local problems, mine privatized data, prune spurious information, simplify complex results, how to update models for new platforms, and more. Chapters share largely applicable experimental results discussed with the blend of practitioner focused domain expertise, with commentary that highlights the methods that are most useful, and applicable to the widest range of projects. Each chapter is written by a prominent expert and offers a state-of-the-art solution to an identified problem facing data scientists in software engineering. Throughout, the editors share best practices collected from their experience training software engineering students and practitioners to master data science, and highlight the methods that are most useful, and applicable to the widest range of projects. 406 pp. Englisch.
Librería: AHA-BUCH GmbH, Einbeck, Alemania
EUR 156,09
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Añadir al carritoTaschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Data Science for Software Engineering: Sharing Data and Models presents guidance and procedures for reusing data and models between projects to produce results that are useful and relevant. Starting with a background section of practical lessons and warnings for beginner data scientists for software engineering, this edited volume proceeds to identify critical questions of contemporary software engineering related to data and models. Learn how to adapt data from other organizations to local problems, mine privatized data, prune spurious information, simplify complex results, how to update models for new platforms, and more. Chapters share largely applicable experimental results discussed with the blend of practitioner focused domain expertise, with commentary that highlights the methods that are most useful, and applicable to the widest range of projects. Each chapter is written by a prominent expert and offers a state-of-the-art solution to an identified problem facing data scientists in software engineering. Throughout, the editors share best practices collected from their experience training software engineering students and practitioners to master data science, and highlight the methods that are most useful, and applicable to the widest range of projects.