Linear Algebra, Matrix Algebra, and Linear Systems
Reference documents in Linear Algebra, Matrix Algebra, and Linear Systems.
Documents in Linear Algebra, Matrix Algebra, and Linear Systems
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Linear Algebra: Foundations to Frontiers (LAFF) is packed full of challenging, rewarding material that is essential for mathematicians, engineers, scientists, and anyone working with large datasets.
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The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts.
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This book is intended as a text for a graduate course that focuses on applications of linear algebra and on the algorithms used to solve the problems that arise in those applications. Often the particular nature of the applications will prompt us to seek algorithms with particular properties; we then turn to the matrix theory to understand the workings of the algorithms.
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This book presents methods for the computational solution of some important problems of linear algebra: linear systems, linear least squares problems, eigenvalue problems, and linear programming problems.
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This book applies the linear algebra concepts to optimization problems which frequently occur throughout machine learning. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm.
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This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering.
