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• W2065236031 abstract "Astronomy and astrophysics are witnessing dramatic increases in data volume as detectors, telescopes and computers become ever more powerful. During the last decade, sky surveys across the electromagnetic spectrum have collected hundreds of terabytes of astronomical data for hundreds of millions of sources. Over the next decade, the data volume will enter the petabyte domain, and provide accurate measurements for billions of sources. Astronomy and physics students are not traditionally trained to handle such voluminous and complex data sets. In this paper we describe astroML; an initiative, based on python and scikit-learn, to develop a compendium of machine learning tools designed to address the statistical needs of the next generation of students and astronomical surveys. We introduce astroML and present a number of example applications that are enabled by this package." @default.
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• W2065236031 date "2012-10-01" @default.
• W2065236031 modified "2023-10-17" @default.
• W2065236031 title "Introduction to astroML: Machine learning for astrophysics" @default.
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• W2065236031 doi "https://doi.org/10.1109/cidu.2012.6382200" @default.
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