Machine Learning for Astrophysics 2024: Proceedings of the 2nd Ml4astro International Conference 8-12 July 2024 - Couverture rigide

9783032022318: Machine Learning for Astrophysics 2024: Proceedings of the 2nd Ml4astro International Conference 8-12 July 2024

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ISBN 10 : 3032022312 ISBN 13 : 9783032022318

Editeur : Springer Nature Switzerland AG, 2026

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This proceedings book reviews the state of the art in the exploitation of machine learning techniques for the astrophysics community and gives the reader a complete overview of the field. The contributed chapters allow the reader to easily digest the material through balanced theoretical and numerical methods and tools with applications in different fields of theoretical and observational astronomy. The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics.

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�diteur: Springer Nature Switzerland AG
Date d'�dition: 2026
Langue: anglais
ISBN 10: 3032022312
ISBN 13: 9783032022318
Reliure: Reli�
Nombre de pages: 205
�diteur: Bufano Filomena, Sciacca Eva, Riggi Simone
Coordonn�es du fabricant: Springer Nature Customer Service Center GmbH
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Machine Learning for Astrophysics 2024

Edit� par Springer Verlag GmbH, 2026

ISBN 10 : 3032022312 ISBN 13 : 9783032022318

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Machine Learning for Astrophysics 2024

Filomena Bufano

Edit� par Springer, Berlin, Springer M�r 2026, 2026

ISBN 10 : 3032022312 ISBN 13 : 9783032022318

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Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne

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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This proceedings book reviews the state of the art in the exploitation of machine learning techniques for the astrophysics community and gives the reader a complete overview of the field. The contributed chapters allow the reader to easily digest the material through balanced theoretical and numerical methods and tools with applications in different fields of theoretical and observational astronomy. The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics. 190 pp. Englisch. N� de r�f. du vendeur 9783032022318

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Machine Learning for Astrophysics 2024

Filomena Bufano

Edit� par Springer, Springer Apr 2026, 2026

ISBN 10 : 3032022312 ISBN 13 : 9783032022318

Neuf Couverture rigide

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This proceedings book reviews the state of the art in the exploitation of machine learning techniques for the astrophysics community and gives the reader a complete overview of the field. The contributed chapters allow the reader to easily digest the material through balanced theoretical and numerical methods and tools with applications in different fields of theoretical and observational astronomy. The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 208 pp. Englisch. N� de r�f. du vendeur 9783032022318

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Machine Learning for Astrophysics 2024 : Proceedings of the 2nd ML4ASTRO International Conference 8-12 July 2024

Eva Sciacca

Edit� par Springer, 2026

ISBN 10 : 3032022312 ISBN 13 : 9783032022318

Neuf Couverture rigide

Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne

�valuation du vendeur 5 sur 5 �toiles

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This proceedings book reviews the state of the art in the exploitation of machine learning techniques for the astrophysics community and gives the reader a complete overview of the field. The contributed chapters allow the reader to easily digest the material through balanced theoretical and numerical methods and tools with applications in different fields of theoretical and observational astronomy. The book helps the reader to really understand and quantify both the opportunities and limitations of using machine learning in several fields of astrophysics. N� de r�f. du vendeur 9783032022318

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