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Machine Learning for Earth Sciences / Maurizio Petrelli (C 2023)

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Titre : Machine Learning for Earth Sciences : Using Python to Solve Geological Problems
Type de document : texte imprimé
Auteurs : Maurizio Petrelli, Auteur
Editeur : Cham : Springer International Publishing AG
Année de publication : C 2023
Collection : Springer Textbooks in Earth Sciences, Geography and Environment
Importance : 1 volume (xvi-209 pages)
Présentation : illustrations, couverture illustr?ee en couleurs
Format : 24 cm
ISBN/ISSN/EAN : 978-3-031-35116-7
Langues : Anglais (eng)
Catégories : Angewandte Informatik ; Angewandte Mathematik ; Applied mathematics ; Apprentissage automatique ; Apprentissage profond ; COMPUTERS / Artificial Intelligence ; COMPUTERS / Enterprise Applications ; G?eologie ; Geowissenschaften ; Information technology: general issues ; Intelligence artificielle
Capability of a device to perform functions normally associated with human intelligence.
; K?Eunstliche Intelligenz ; Machine learning ; Maschinelles Lernen ; MATHEMATICS / Applied ; MATHEMATICS / Probability & Statistics / General ; Python (langage de programmation) ; SCIENCE / Earth Sciences / General ; Sciences de la terre

Résumé : This textbook introduces the reader to Machine Learning (ML) applications in Earth Sciences. In detail, it starts by describing the basics of machine learning and its potentials in Earth Sciences to solve geological problems. It describes the main Python tools devoted to ML, the typical workflow of ML applications in Earth Sciences, and proceeds with reporting how ML algorithms work. The book provides many examples of ML application to Earth Sciences problems in many fields, such as the clustering and dimensionality reduction in petro-volcanological studies, the clustering of multi-spectral data, well-log data facies classification, and machine learning regression in petrology. Also, the book introduces the basics of parallel computing and how to scale ML models in the cloud. The book is devoted to Earth Scientists, at any level, from students to academics and professionals

Machine Learning for Earth Sciences : Using Python to Solve Geological Problems [texte imprimé] / Maurizio Petrelli, Auteur . - Cham : Springer International Publishing AG, C 2023 . - 1 volume (xvi-209 pages) : illustrations, couverture illustr?ee en couleurs ; 24 cm. - (Springer Textbooks in Earth Sciences, Geography and Environment) .
ISBN : 978-3-031-35116-7
Langues : Anglais (eng)
Catégories : Angewandte Informatik ; Angewandte Mathematik ; Applied mathematics ; Apprentissage automatique ; Apprentissage profond ; COMPUTERS / Artificial Intelligence ; COMPUTERS / Enterprise Applications ; G?eologie ; Geowissenschaften ; Information technology: general issues ; Intelligence artificielle
Capability of a device to perform functions normally associated with human intelligence.
; K?Eunstliche Intelligenz ; Machine learning ; Maschinelles Lernen ; MATHEMATICS / Applied ; MATHEMATICS / Probability & Statistics / General ; Python (langage de programmation) ; SCIENCE / Earth Sciences / General ; Sciences de la terre

Résumé : This textbook introduces the reader to Machine Learning (ML) applications in Earth Sciences. In detail, it starts by describing the basics of machine learning and its potentials in Earth Sciences to solve geological problems. It describes the main Python tools devoted to ML, the typical workflow of ML applications in Earth Sciences, and proceeds with reporting how ML algorithms work. The book provides many examples of ML application to Earth Sciences problems in many fields, such as the clustering and dimensionality reduction in petro-volcanological studies, the clustering of multi-spectral data, well-log data facies classification, and machine learning regression in petrology. Also, the book introduces the basics of parallel computing and how to scale ML models in the cloud. The book is devoted to Earth Scientists, at any level, from students to academics and professionals

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Python recipes for earth sciences / Martin H Trauth (C 2022)

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Titre : Python recipes for earth sciences
Type de document : texte imprimé
Auteurs : Martin H Trauth (1963-..), Auteur
Editeur : Cham : Springer
Année de publication : C 2022
Collection : Springer Textbooks in Earth Sciences, Geography and Environment, ISSN 2510-1307
Importance : 1 vol. (XII-453 p.)
Présentation : ill. en coul., couv. ill. en coul
Format : 24 cm
ISBN/ISSN/EAN : 978-3-031-07718-0
Langues : Anglais (eng)
Catégories : G?eologie ; Python (Computer program language) ; Python (langage de programmation) ; Sciences de la terre

Résumé : Python is used in a wide range of geoscientific applications, such as in processing images for remote sensing, in generating and processing digital elevation models, and in analyzing time series. This book introduces methods of data analysis in the geosciences using Python that include basic statistics for univariate, bivariate, and multivariate data sets, time series analysis, and signal processing; the analysis of spatial and directional data; and image analysis. The text includes numerous examples that demonstrate how Python can be used on data sets from the earth sciences. The supplementary electronic material (available online through Springer Link) contains the example data as well as recipes that include all the Python commands featured in the book

Python recipes for earth sciences [texte imprimé] / Martin H Trauth (1963-..), Auteur . - Cham : Springer, C 2022 . - 1 vol. (XII-453 p.) : ill. en coul., couv. ill. en coul ; 24 cm. - (Springer Textbooks in Earth Sciences, Geography and Environment, ISSN 2510-1307) .
ISBN : 978-3-031-07718-0
Langues : Anglais (eng)
Catégories : G?eologie ; Python (Computer program language) ; Python (langage de programmation) ; Sciences de la terre

Résumé : Python is used in a wide range of geoscientific applications, such as in processing images for remote sensing, in generating and processing digital elevation models, and in analyzing time series. This book introduces methods of data analysis in the geosciences using Python that include basic statistics for univariate, bivariate, and multivariate data sets, time series analysis, and signal processing; the analysis of spatial and directional data; and image analysis. The text includes numerous examples that demonstrate how Python can be used on data sets from the earth sciences. The supplementary electronic material (available online through Springer Link) contains the example data as well as recipes that include all the Python commands featured in the book

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