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Verlag: Springer International Publishing AG, Cham, 2024
ISBN 10: 3031700074 ISBN 13: 9783031700071
Sprache: Englisch
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In den WarenkorbPaperback. Zustand: new. Paperback. This book is a self-contained introduction to engineering and testing machine learning (ML) systems. It systematically discusses and teaches the art of crafting and developing software systems that include and surround machine learning models. Crafting ML based systems that are business-grade is highly challenging, as it requires statistical control throughout the complete system development life cycle. To this end, the book introduces an experiment first approach, stressing the need to define statistical experiments from the beginning of the development life cycle and presenting methods for careful quantification of business requirements and identification of key factors that impact business requirements. Applying these methods reduces the risk of failure of an ML development project and of the resultant, deployed ML system. The presentation is complemented by numerous best practices, case studies and practical as well as theoretical exercises and their solutions, designed to facilitate understanding of the ideas, concepts and methods introduced.The goal of this book is to empower scientists, engineers, and software developers with the knowledge and skills necessary to create robust and reliable ML software. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Verlag: Lanzhou University Press Pub. Date :2009-03, 2000
ISBN 10: 7311031788 ISBN 13: 9787311031787
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In den Warenkorbpaperback. Zustand: New. Paperback. Pub Date: 2009 Pages: 298 in Publisher: Lanzhou University Press' ordinary institutions of higher learning of economics and management planning materials. management information systems: Theory and Practice (2nd Edition) is divided into two parts. the first part of from the first nine chapters. the core theoretical knowledge of management information systems; second part of Chapter 10 and Chapter 11. belongs to the practice of teaching content. Contents: Chapter 1 Information Syste.
Verlag: Springer-Nature New York Inc, 2024
ISBN 10: 3031700074 ISBN 13: 9783031700071
Sprache: Englisch
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ISBN 10: 3031700074 ISBN 13: 9783031700071
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In den WarenkorbTaschenbuch. Zustand: Neu. Neuware -This book is a self-contained introduction to engineering and testing machine learning (ML) systems. It systematically discusses and teaches the art of crafting and developing software systems that include and surround machine learning models. Crafting ML based systems that are business-grade is highly challenging, as it requires statistical control throughout the complete system development life cycle. To this end, the book introduces an ¿experiment first¿ approach, stressing the need to define statistical experiments from the beginning of the development life cycle and presenting methods for careful quantification of business requirements and identification of key factors that impact business requirements. Applying these methods reduces the risk of failure of an ML development project and of the resultant, deployed ML system. The presentation is complemented by numerous best practices, case studies and practical as well as theoretical exercises and their solutions, designed to facilitate understanding of the ideas, concepts and methods introduced.The goal of this book is to empower scientists, engineers, and software developers with the knowledge and skills necessary to create robust and reliable ML software.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 196 pp. Englisch.
Verlag: Springer Nature Switzerland, Springer Nature Switzerland, 2024
ISBN 10: 3031700074 ISBN 13: 9783031700071
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In den WarenkorbTaschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book is a self-contained introduction to engineering and testing machine learning (ML) systems. It systematically discusses and teaches the art of crafting and developing software systems that include and surround machine learning models. Crafting ML based systems that are business-grade is highly challenging, as it requires statistical control throughout the complete system development life cycle. To this end, the book introduces an 'experiment first' approach, stressing the need to define statistical experiments from the beginning of the development life cycle and presenting methods for careful quantification of business requirements and identification of key factors that impact business requirements. Applying these methods reduces the risk of failure of an ML development project and of the resultant, deployed ML system. The presentation is complemented by numerous best practices, case studies and practical as well as theoretical exercises and their solutions, designed to facilitate understanding of the ideas, concepts and methods introduced.The goal of this book is to empower scientists, engineers, and software developers with the knowledge and skills necessary to create robust and reliable ML software.
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Verlag: Wiley-Blackwell 2010-04-28, 2010
ISBN 10: 047039983X ISBN 13: 9780470399835
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