Revising this textbook has been a special challenge, for a very nice reason. So many people have read this book, and taught from it, and even loved it. The spirit of the book could never change. This text was written to help our teaching of linear algebra keep up with the enormous importance of this subject—which just continues to grow.

《代数(英文版)(第2版)》由著名代数学家与代数几何学家MichaelArtin所著，是作者在代数领域数十年的智慧和经验的结晶。书中既介绍了矩阵运算、群、向量空间、线性算子、对称等较为基本的内容，又介绍了环、模型、域、伽罗瓦理论等较为高深的内容。《代数(英文版)(第2版)》对于提高数学理解能力，增强对代数的兴趣是非常有益处的。此外，《代数(英文版)(第2版)》的可阅读性强，书中的习题也很有针对性，能让读者很快地掌握分析和思考的方法？作者结合这20年来的教学经历及读者的反馈，对本版进行了全面更新，更强调对称性、线性群、二次数域和格等具体主题。本版的具体更新情况如下：新增球面、乘积环和因式分解的计算方法等内容，并补充给出一些结论的证明，如交错群是简单的、柯西定理、分裂定理等。修订了对对应定理、SU2表示、正交关系等内容的讨论，并把线性变换和因子分解都拆分为两章来介绍。新增大量习题，并用星号标注出具有挑战性的习题。《代数(英文版)(第2版)》在麻省理工学院、普林斯顿大学、哥伦比亚大学等著名学府得到了广泛采用，是代数学的经典教材之一。

Introduction to Linear Algebra 5th 2016，是高清版本的，而且还有习题答案，以及官网的toc，方便大家使用

LINEAR ALGEBRA AND ITS APPLICATIONS FOURTH EDITION SOLUTIONS MANUAL David C. Lay

非常适合于搞机器视觉方向的小伙伴，入门快，讲解清晰，值得学习！如果没有博客账号或积分，可以直接@我，邮箱：1270978696@qq.com。免费分享，共同研究！

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。

代数学引论第二版第四章部分答案，除了最后少数题目外的答案都有

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。