Items related to Machine Learning for Planetary Science
- PublisherElsevier
- Publication date2022
- ISBN 10 0128187212
- ISBN 13 9780128187210
- BindingPaperback
- LanguageEnglish
- Edition number1
- Number of pages232
- EditorHelbert Joern, D'Amore Mario, Aye Michael, Kerner Hannah
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Machine Learning for Planetary Science
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Machine Learning for Planetary Science
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Machine Learning for Planetary Science
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Machine Learning for Planetary Science
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Elsevier Science & Technology, Elsevier, 2022
ISBN 10: 0128187212
ISBN 13: 9780128187210
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. Englisch. Seller Inventory # 9780128187210
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Machine Learning for Planetary Science
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Machine Learning for Planetary Science
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Elsevier Science & Technology, Elsevier, 2022
ISBN 10: 0128187212
ISBN 13: 9780128187210
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. Seller Inventory # 9780128187210
Quantity: 2 available
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Machine Learning for Planetary Science
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Machine Learning for Planetary Science
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Elsevier Science & Technology|Elsevier, 2022
ISBN 10: 0128187212
ISBN 13: 9780128187210
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores r. Seller Inventory # 369558048
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