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Lingua: Inglese
Editore: Taylor & Francis Ltd, London, 2025
ISBN 10: 0367255065 ISBN 13: 9780367255060
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Paperback. Condizione: new. Paperback. Machine learning has redefined the way we work with data and is increasingly becoming an indispensable part of everyday life. The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions discusses how modern software engineering practices are part of this revolution both conceptually and in practical applictions.Comprising a broad overview of how to design machine learning pipelines as well as the state-of-the-art tools we use to make them, this book provides a multi-disciplinary view of how traditional software engineering can be adapted to and integrated with the workflows of domain experts and probabilistic models.From choosing the right hardware to designing effective pipelines architectures and adopting software development best practices, this guide will appeal to machine learning and data science specialists, whilst also laying out key high-level principlesin a way that is approachable for students of computer science and aspiring programmers. Machine learning has redefined the way we work with data and is increasingly becoming an indispensable part of everyday life, yet software engineering has played a remarkably small role compared to other disciplines. This book addresses such a disparity. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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ISBN 10: 0367255065 ISBN 13: 9780367255060
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Aggiungi al carrelloPaperback. Condizione: New.
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ISBN 10: 0367255065 ISBN 13: 9780367255060
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Aggiungi al carrelloPaperback. Condizione: New. Machine learning has redefined the way we work with data and is increasingly becoming an indispensable part of everyday life. The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions discusses how modern software engineering practices are part of this revolution both conceptually and in practical applictions.Comprising a broad overview of how to design machine learning pipelines as well as the state-of-the-art tools we use to make them, this book provides a multi-disciplinary view of how traditional software engineering can be adapted to and integrated with the workflows of domain experts and probabilistic models.From choosing the right hardware to designing effective pipelines architectures and adopting software development best practices, this guide will appeal to machine learning and data science specialists, whilst also laying out key high-level principlesin a way that is approachable for students of computer science and aspiring programmers.
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Aggiungi al carrelloPaperback. Condizione: Brand New. 346 pages. 9.19x6.13x9.21 inches. In Stock.
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Aggiungi al carrelloCondizione: New. Marco Scutari is a Senior Researcher at Istituto Dalle Molle di Studi sull Intelligenza Artificiale (IDSIA), Switzerland. He has held positions in statistics, statistical genetics and machine learning in the UK and Switzerland since comp.
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Da: preigu, Osnabrück, Germania
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. The Pragmatic Programmer for Machine Learning | Engineering Analytics and Data Science Solutions | Marco Scutari (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2025 | Chapman and Hall/CRC | EAN 9780367255060 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Lingua: Inglese
Editore: Taylor and Francis Ltd, GB, 2025
ISBN 10: 0367255065 ISBN 13: 9780367255060
Da: Rarewaves.com UK, London, Regno Unito
EUR 68,60
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Aggiungi al carrelloPaperback. Condizione: New. Machine learning has redefined the way we work with data and is increasingly becoming an indispensable part of everyday life. The Pragmatic Programmer for Machine Learning: Engineering Analytics and Data Science Solutions discusses how modern software engineering practices are part of this revolution both conceptually and in practical applictions.Comprising a broad overview of how to design machine learning pipelines as well as the state-of-the-art tools we use to make them, this book provides a multi-disciplinary view of how traditional software engineering can be adapted to and integrated with the workflows of domain experts and probabilistic models.From choosing the right hardware to designing effective pipelines architectures and adopting software development best practices, this guide will appeal to machine learning and data science specialists, whilst also laying out key high-level principlesin a way that is approachable for students of computer science and aspiring programmers.
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EUR 140,53
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Aggiungi al carrelloHardcover. Condizione: Brand New. 346 pages. 9.19x6.13x1.02 inches. In Stock.