Linear Algebra for Computer Vision, Robotics, and Machine Learning by Jean Gallier. Jocelyn Quaintance
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About this book :-
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.
Book Detail :-
This book has following details information.
Title:
Linear Algebra for Computer Vision, Robotics, and Machine Learning by Jean Gallier. Jocelyn Quaintance by NA
Publisher:
University of Pennsylvania
Series:
freeCompBooks
Year:
2024
Pages:
787
Type:
PDF
Language:
English
ISBN-10 #:
9811206392
ISBN-13 #:
978-9811206399
Country:
Pakistan
License:
N\A
Get this book from Amazon
About Author :-
The author NA
NA
Book Contents :-
conver the following topics.
1. Introduction
2. Vector Spaces, Bases, Linear Maps
3. Matrices and Linear Maps
4 Haar Bases, Haar Wavelets, Hadamard Matrices
5. Direct Sums, Rank-Nullity Theorem, Affine Maps
6. Determinants
7. Gaussian Elimination, LU, Cholesky, Echelon Form
8. Vector Norms and Matrix Norms
9. Iterative Methods for Solving Linear Systems
10. The Dual Space and Duality
11. Euclidean Spaces
12. QR-Decomposition for Arbitrary Matrices
13. Hermitian Spaces
14. Eigenvectors and Eigenvalues
15. Unit Quaternions and Rotations in SO(3)
16. Spectral Theorems
17. Computing Eigenvalues and Eigenvectors
18. Graphs and Graph Laplacians; Basic Facts
19. Spectral Graph Drawing
20. Singular Value Decomposition and Polar Form
21. Applications of SVD and Pseudo-Inverses
22. Annihilating Polynomials; Primary Decomposition
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