Linear Algebra and Its Applications (6版)

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Linear Algebra and Its Applications, Global Edition ISBN13：9781292351216 出版社：Pearson Education Limited 作者：David Lay;Steven Lay;Judi McDonald 裝訂／頁數：平裝／672頁 版次：6 出版日：2021/07/14

Linear Algebra and Optimization with Applications to Machine Learning Vol.I: Linear Algebra for Computer Vision, Robotics, and Machine Learning

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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. 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. Sample Chapter(s) Preface Chapter 1: Introduction Request Inspection Copy Contents: Introduction Vector Spaces, Bases, Linear Maps Matrices and Linear Maps Haar Bases, Haar Wavelets, Hadamard Matrices Direct Sums, Rank-Nullity Theorem, Affine Maps Determinants Gaussian Elimination, LU-Factorization, Cholesky Factorization, Reduced Row Echelon Form Vector Norms and Matrix Norms Iterative Methods for Solving Linear Systems The Dual Space and Duality Euclidean Spaces QR-Decomposition for Arbitrary Matrices Hermitian Spaces Eigenvectors and Eigenvalues Unit Quaternions and Rotations in SO(3) Spectral Theorems in Euclidean and Hermitian Spaces Computing Eigenvalues and Eigenvectors Graphs and Graph Laplacians; Basic Facts Spectral Graph Drawing Singular Value Decomposition and Polar Form Applications of SVD and Pseudo-Inverses Annihilating Polynomials and the Primary Decomposition Bibliography Index Readership: Undergraduate and graduate students interested in mathematical fundamentals of linear algebra in computer vision, machine learning, robotics, applied mathematics, and electrical engineering.

Linear Algebra and Its Applications (4版)

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書名：Linear Algebra and Its Applications 4/e 作者：STRANG 出版社：Cengage 出版日期：2006/00/00 ISBN：9780030105678 內容簡介 ●Offers a large number of applications to physics, engineering, probability and statistics, economics, and biology. These are not tacked on at the end; they are part of the mathematics. ●Includes an optional section on the Fast Fourier Transform. Students discover how this outstanding algorithm fits into linear algebra and introduces complex numbers. ●Recognizes what the computer can do in linear algebra, without being dominated by it. ●Contains a wealth of exercises that appear in the sections and in the chapter reviews. 目錄 1. MATRICES AND GAUSSIAN ELIMINATION. 2. VECTOR SPACES. 3. ORTHOGONALITY. 4. DETERMINANTS. 5. EIGENVALUES AND EIGENVECTORS. 6. POSITIVE DEFINITE MATRICES. 7. COMPUTATIONS WITH MATRICES. 8. LINEAR PROGRAMMING AND GAME THEORY. Appendix A: Intersection, Sum, and Product of Spaces. Appendix B: The Jordan Form. Solutions to Selected Exercises.

Linear Algebra and Learning from Data

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Linear Algebra and Learning from Data ISBN13：9780692196380 出版社：Cambridge Univ Pr 作者：Gilbert Strang 裝訂：精裝 出版日：2019/02/28 內容簡介 ●The first textbook designed to teach linear algebra as a tool for deep learning ●From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra ●Includes the necessary background from statistics and optimization ●Explains stochastic gradient descent, the key algorithim of deep learning, in detail 目錄 Deep learning and neural nets Preface and acknowledgements Part I. Highlights of Linear Algebra Part II. Computations with Large Matrices Part III. Low Rank and Compressed Sensing Part IV. Special Matrices Part V. Probability and Statistics Part VI. Optimization Part VII. Learning from Data Books on machine learning Eigenvalues and singular values: Rank One Codes and algorithms for numerical linear algebra Counting parameters in the basic factorizations