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Preface I wrote this book to help machine learning practitioners, like you, get on top of linear algebra, fast. Linear Algebra Is Important in Machine Learning There is no doubt that linear algebra is important in machine learning. Linear algebra is the mathematics of data. It’s all vectors and matrices of numbers. Modern statistics is described using the notation of linear algebra and modern statistical methods harness the tools of linear algebra. Modern machine learning methods are described the same way, using the notations and tools drawn directly from linear algebra. Even some classical methods used in the field, such as linear regression via linear least squares and singular-value decomposition, are linear algebra methods, and other methods, such as principal component analysis, were born from the marriage of linear algebra and statistics. To read and understand machine learning, you must be able to read and understand linear algebra. Practitioners Study Linear Algebra Too Early If you ask how to get started in machine learning, you will very likely be told to start with linear algebra. We know that knowledge of linear algebra is critically important, but it does not have to be the place to start. Learning linear algebra first, then calculus, probability, statistics, and eventually machine learning theory is a long and slow bottom-up path. A better fit for developers is to start with systematic procedures that get results, and work back to the deeper understanding of theory, using working results as a context. I call this the top-down or results-first approach to machine learning, and linear algebra is not the first step, but perhaps the second or third. Practitioners Study Too Much Linear Algebra When practitioners do circle back to study linear algebra, they learn far more of the field than is required for or relevant to machine learning. Linear algebra is a large field of study that has tendrils into engineering, physics and quantum physics. There are also

No bullshit guide to linear algebra, 绝对好书。 无废话学线性代数。 机器学习最好的线代入门书籍。

代数学引论第二版第三章答案，包含了大部分网上能查到的答案以及自己写的部分。

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由Steven J.Leon写的《Linear Algebra with Applications（9th edition）》中英文版。 英文版是高清的，文字可拷；中文版是扫描的，但很清晰。 英文版还附有在网站上的第8章和第9章，但是中文版没有将这两章翻译出来。 欢迎下载。

一本很好的神书，基本上学习线性代数， 都需要从这本书开始看

Differential Equations and Linear Algebra (4th Edition) By 作者: C. Henry Edwards – David E. Penney – David Calvis ISBN-10 书号: 013449718X ISBN-13 书号: 9780134497181 Edition 版本: 4 出版日期: 2017-01-14 pages 页数: (768 ) Pearson纯净原版超清 $204.4 For courses in Differential Equations and Linear Algebra. Concepts, methods, and core topics covering elementary differential equations and linear algebra through real-world applications In a contemporary introduction to differential equations and linear algebra, acclaimed authors Edwards and Penney combine core topics in elementary differential equations with concepts and methods of elementary linear algebra. Renowned for its real-world applications and blend of algebraic and geometric approaches, Differential Equations and Linear Algebra introduces you to mathematical modeling of real-world phenomena and offers the best problems sets in any differential equations and linear algebra textbook. The 4th Edition includes fresh new computational and qualitative flavor evident throughout in figures, examples, problems, and applications. Additionally, an Expanded Applications website containing expanded applications and programming tools is now available. Contents Application Modules Preface 1 First-Order Differential Equations 2 Mathematical Models and Numerical Methods 3Linear Systemsand Matrices 4Vector Spaces 5Higher-Order Linear Differential Equations 6Eigenvalues and Eigenvectors 7 Linear Systems of Differential Equations 8 Matrix Exponential Methods 9 Nonlinear Systems and Phenomena 10 Laplace Transform Methods 11 Power Series Methods References for Further Study Appendix A:Existence and Uniqueness of Solutions Appendix B:Theory of Determinants Answers to Selected Problems

《Applied Numerical Linear Algebra 》和《应用数值线性代数》；中英两本；［美］James W. Demmel 著 ；

数学专业，研究生攻读代数方向的同学的基础课程，需要考博的同学也会需要