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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.
Acerca del autor:
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 completing his PhD in statistics in 2011. His research focuses on the theory of Bayesian networks and their applications to biological and clinical data, as well as statistical computing and software engineering.
Mauro Malvestio is a senior technologist based in Milan, Italy, with more than 15 years of experience in software engineering and IT operations in consulting and product companies as a CTO. His research focuses on software engineering, machine learning systems, embedded systems and cloud computing.
Título: The Pragmatic Programmer for Machine ...
Editorial: Chapman and Hall/CRC
Año de publicación: 2025
Encuadernación: paperback
Condición: New
Edición: 1st Edition.
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 132436899
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 358 pp. Englisch. Nº de ref. del artículo: 9780367255060
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