Wang Chisheng (30 results)

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Hardcover. Condition: Brand New. 132 pages. 6.30x0.55x9.49 inches. In Stock.

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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning has become a cornerstone of modern data-driven science and technology. For mathematics students and researchers, understanding the mathematical foundations behind machine learning is essential, even if they never work directly with real-world datasets.This book provides a rigorous yet accessible introduction to the core mathematical ideas that underpin machine learning. Topics such as linear and nonlinear regression, regularization techniques, and the fundamentals of neural networks are explained in detail from a clear mathematical perspective.Unlike many existing texts that emphasize coding and practical implementation, this book focuses on theoretical results and conceptual understanding. It is designed for readers who want to grasp the mathematics behind machine learning without writing code.Who should read this book Mathematics students and researchers interested in machine learning but with little programming experience.Scientists and engineers who have applied machine learning in practice and now seek a deeper understanding of its mathematical foundations.…

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Hardcover. Condition: new. Hardcover. Interferometric synthetic aperture radar (InSAR) has demonstrated its potential in monitoring geological disasters, e.g., those related to subsidence, landslides, earthquakes, and volcanoes. Such monitoring results provide significant information for further physical modeling, driving mechanism interpretation, developments in early warning technology, and the management and formulation of policies by authorities and stakeholders. This reprint focuses on the monitoring and modeling of geological disasters using InSAR observations, with content covering topics such as PS/DS processing, deformation parameter inversion, motoring deformation (e.g., earthquakes, volcanoes, and oil extraction), and driving mechanism interpretation. These excellent reports significantly contribute to further developing the monitoring and modeling of geological disasters using InSAR techniques. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Machine learning has become a cornerstone of modern data-driven science and technology. For mathematics students and researchers, understanding the mathematical foundations behind machine learning is essential, even if they never work directly with real-world datasets.This book provides a rigorous yet accessible introduction to the core mathematical ideas that underpin machine learning. Topics such as linear and nonlinear regression, regularization techniques, and the fundamentals of neural networks are explained in detail from a clear mathematical perspective.Unlike many existing texts that emphasize coding and practical implementation, this book focuses on theoretical results and conceptual understanding. It is designed for readers who want to grasp the mathematics behind machine learning without writing code.Who should read this book - Mathematics students and researchers interested in machine learning but with little programming experience.- Scientists and engineers who have applied machine learning in practice and now seek a deeper understanding of its mathematical foundations.Springer Nature Customer Service Center GmbH, Europaplatz 3,69115 Heidelberg, Germany, Heidelberg 136 pp. Englisch. …

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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Interferometric synthetic aperture radar (InSAR) has demonstrated its potential in monitoring geological disasters, e.g., those related to subsidence, landslides, earthquakes, and volcanoes. Such monitoring results provide significant information for further physical modeling, driving mechanism interpretation, developments in early warning technology, and the management and formulation of policies by authorities and stakeholders. This reprint focuses on the monitoring and modeling of geological disasters using InSAR observations, with content covering topics such as PS/DS processing, deformation parameter inversion, motoring deformation (e.g., earthquakes, volcanoes, and oil extraction), and driving mechanism interpretation. These excellent reports significantly contribute to further developing the monitoring and modeling of geological disasters using InSAR techniques.…

Buch. Condition: Neu. Monitoring and Modelling of Geological Disasters Based on InSAR Observations II | Buch | Englisch | 2025 | MDPI AG | EAN 9783725853632 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Buch. Condition: Neu. Monitoring and Modelling of Geological Disasters Based on InSAR Observations | Buch | Gebunden | Englisch | 2023 | MDPI AG | EAN 9783036563800 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…

Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Interferometric synthetic aperture radar (InSAR) has demonstrated its potential in monitoring geological disasters, e.g., in relation to subsidence, landslides, earthquakes, and volcanoes. Such monitoring results provide significant information for further physical modeling, driving mechanism interpretation, developments in early warning technology, and the management and formulation of policies by relevant authorities and stakeholders. This reprint focuses on monitoring and modelling of geological disasters using InSAR observations. The content covers topics such as PS/DS processing, deformation parameter inversion, motoring deformation (e.g., earthquakes, volcanoes, and oil extraction), and driving mechanism interpretation. These excellent reports significantly contribute to further developments in the monitoring and modeling of geological disasters using InSAR techniques.…