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010 _a 2020001727
020 _a9781944660345
040 _aLBSOR/DLC
_beng
_cDLC
_erda
_dDLC
_dIIITD
042 _apcc
050 0 0 _aQA184.2
_b.G35 2020
082 0 0 _a512.502
_223
_bGAL-L
100 1 _aGallier, Jean
245 1 0 _aLinear algebra and optimization with applications to machine learning :
_cby Jean Gallier and Jocelyn Quaintance
_blinear algebra for computer vision, robotics, and machine learning, vol I
260 _aSingapore :
_bWorld Scientific,
_c©2023
300 _axv, 806 p. :
_bill. ;
_c23 cm
490 _aLinear algebra and optimization with applications to machine learning ;
_v1
504 _aIncludes bibliographical references and index.
505 1 _aVolume I. Linear algebra for computer vision, robotics, and machine learning -- Volume II. Fundamentals of optimization theory with applications to machine learning
520 _a"This book provides the mathematical fundamentals of linear algebra to practicers 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"--
650 0 _aAlgebras, Linear.
650 0 _aMachine learning
_xMathematics.
650 0 _aRobotics
650 0 _aAlgebras, Linear -- Data processing
650 0 _aComputer science -- Mathematics
650 0 _aComputer - aided design
650 0 _aMachine learning
650 0 _aAlgebras, Linear
650 0 _aComputer vision
700 1 _aQuaintance, Jocelyn
906 _a7
_bcbc
_corignew
_d1
_eecip
_f20
_gy-gencatlg
942 _2ddc
_cBK
_01
999 _c171284
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