Machine Learning in Astronomy (IAU S368). Possibilities and Pitfalls, Hardback/***
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IAU S368 addresses graduate students and professional astronomers who wish to leverage machine learning to unlock the potential of modern data-rich surveys and deep images, as well as archival data. Researchers at the frontiers share best practices in applied machine learning that are relevant to astronomy and other data-rich fields. Publisher: Cambridge University Press Illustration(s): Worked examples or Exercises Number of pages: 200 Collection: Proceedings of the International Astronomical Union Symposia and Colloquia Publication date: 2025 Dimensions: 178 x 254 x 11 Cover type: Hardback Editor(s): Christopher (Swinburne University of Technology, Victoria) Fluke
- Brand
- Cambridge University Press
- subcategory
- Carte straina/Computing & information technology
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