Machine Learning for Planetary Science is popular PDF and ePub book, written by Joern Helbert in 2022-03-22, it is a fantastic choice for those who relish reading online the Science genre. Let's immerse ourselves in this engaging Science book by exploring the summary and details provided below. Remember, Machine Learning for Planetary Science can be Read Online from any device for your convenience.
Machine Learning for Planetary Science Book PDF Summary
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. Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems Utilizes case studies to illustrate how machine learning methods can be employed in practice
Detail Book of Machine Learning for Planetary Science PDF
- Author : Joern Helbert
- Release : 22 March 2022
- Publisher : Elsevier
- ISBN : 9780128187227
- Genre : Science
- Total Page : 234 pages
- Language : English
- PDF File Size : 11,7 Mb
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