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Linear algebra and optimization with applications to machine learning / By: Jean Gallier & Jocelyn Quaintance

By: Contributor(s): Material type: TextTextLanguage: English Publication details: New Jersey : World Scientific, 2023.Description: Volume 1, xv, 806 pages ; 24 cm. PBISBN:
  • 9781944660345
Subject(s): DDC classification:
  • 512.5 GAL
Summary: This book provides the mathematical fundamentals of linear algebra to practitioners in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.
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Item type Current library Collection Call number Status Barcode
Book Book Ranganathan Library Computer Science and Technology 512.5 GAL (Browse shelf(Opens below)) Available 050331

This book provides the mathematical fundamentals of linear algebra to practitioners in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.

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