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Computational Optimal Transport: With Applications to Data Science (Foundations and Trends(r) in Machine Learning) - Softcover
Synopsis
The goal of Optimal Transport (OT) is to define geometric tools that are useful to compare probability distributions. Their use dates back to 1781.
Recent years have witnessed a new revolution in the spread of OT, thanks to the emergence of approximate solvers that can scale to sizes and dimensions that are relevant to data sciences. Thanks to this newfound scalability, OT is being increasingly used to unlock various problems...
- PublisherNow Publishers
- Publication date2019
- ISBN 10 1680835505
- ISBN 13 9781680835502
- BindingPaperback
- LanguageEnglish
- Number of pages272
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Computational Optimal Transport: With Applications to Data Science (Foundations and Trends(r) in Machine Learning)
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Now Publishers (edition ), 2019
ISBN 10: 1680835505
ISBN 13: 9781680835502
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Computational Optimal Transpor
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Computational Optimal Transport: With Applications to Data Science (Foundations and Trends(r) in Machine Learning)
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Computational Optimal Transport: With Applications to Data Science (Foundations and Trends(r) in Machine Learning)
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Computational Optimal Transport: With Applications to Data Science
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ISBN 10: 1680835505
ISBN 13: 9781680835502
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Computational Optimal Transport: With Applications to Data Science (Foundations and Trends(r) in Machine Learning)
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Computational Optimal Transport: With Applications to Data Science (Foundations and Trends (R) in Machine Learning)
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Computational Optimal Transport: With Applications to Data Science
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Condition: New. Presents an overview of the main theoretical insights that support the practical effectiveness of OT before explaining how to turn these insights into fast computational schemes. This book will be a valuable reference for researchers and students wishing to. Seller Inventory # 274030552
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