Numerical Linear Algebra and Optimization for Data Science
Buch in deiner Nähe kaufen
Beschreibung
This book offers a timely and rigorous contribution at the intersection of numerical linear algebra, optimization, and modern data-science applications. The manuscript stands out for its balanced integration of theoretical foundations and computational practice. It develops core concepts in numerical linear algebra such as matrix factorizations, eigenvalue problems, and iterative methods, while systematically connecting them to optimization techniques central to data science, including gradient-based methods, convex and non-convex optimization and large-scale algorithms. The book includes a strong emphasis on contemporary applications.
This book offers a timely and rigorous contribution at the intersection of numerical linear algebra, optimization, and modern data-science applications. The manuscript stands out for its balanced integration of theoretical foundations and computational practice. It develops core concepts in numerical linear algebra such as matrix factorizations, eigenvalue problems, and iterative methods, while systematically connecting them to optimization techniques central to data science, including gradient-based methods, convex and non-convex optimization and large-scale algorithms. The book includes a strong emphasis on contemporary applications.
Balanced integration of theoretical foundations and computational practice Strong emphasis on contemporary applications Accessible to graduate students and researchers while remaining sufficiently in-depth for specialists
Autor*in
Khalide Jbilou
Themen in »Numerical Linear Algebra and Optimization for Data Science«
Matrix Factorizations Eigenvalue Problems Iterative Methods Gradient-Based Methods Regularization Techniques Dimensionality Reduction Numerical Linear Algebra