Linear algebra is relatively easy for students during the early stages of the course, when the material is presented in a familiar, concrete setting. But when abstract concepts are introduced, students often hit a "brick wall." Instructors seem to agree that certain concepts (such as linear independence, spanning, subspace, vector space, and linear transformations), are not easily understood, and require time to assimilate. Since they are fundamental to the study of linear algebra, students' understanding of these concepts is vital to their mastery of the subject. Lay introduces these concepts early in a familiar, concrete R^n setting, develops them gradually, and returns to them again and again throughout the text. Finally, when discussed in the abstract, these concepts are more accessible. Students' conceptual understanding is reinforced through True/False questions, practice problems, and the use of technology. David Lay changed the face of linear algebra with the execution of this philosophy, and continues his quest to improve the way linear algebra is taught with the new Updated Second Edition. With this update, he builds on this philosophy through increased visualization in the text, vastly enhanced technology support, and an extensive instructor support package. He has added additional figures to the text to help students visualize abstract concepts at key points in the course. A new dedicated CD and Website further enhance the course materials by providing additional support to help students gain command of difficult concepts. The CD, included in the back of the book, contains a wealth of new materials, with a registration coupon allowing access to a password-protected Website. These new materials are tied directly to the text, providing a comprehensive package for teaching and learning linear algebra.
David C. Lay 在美国加利福尼亚大学获得硕士和博士学位。他是马里兰大学帕克学院数学系教授,同时还是阿姆斯特丹大学、阿姆斯特丹自由大学和德国凯泽斯劳滕大学的访问教授。Lay教授是“线性代数课程研究小组”的核心成员,发表了30多篇关于泛函分析和线性代数方面的论文,并与他人合著有多部数学教材。
PCA这么重要的东西应该与SVD一样专门写一段,而不是放在“7.5 图像处理和统计学中的应用”底下当成普通例子来写。虽然这里PCA写的是真清晰真透彻,秒杀网上无数介绍。另外,SVD讲的太简略了,看完公式也抓不住本质。最好加入几何理解角度,并谈谈与PCA的异同。
评分最近想进修一下统计,遇到第一个难关就是线性代数,好多东西都忘得差不多了,只记得某年某月曾算过特征值和特征向量…… 依稀记得当年考研时候用的就是Lay老人家这本书的中文版,但想到自己已经是研究僧了,应该看看原版书了,于是决定厚颜无耻地去爱问上偷书。下...
评分原书可能是好书,但是中文版翻译真是太烂了,奉劝诸位能看英文版的尽量看英文的。 ps:第二页的“两个线性方程组称为等价的.若它们有相同的解集.”这是高中生的翻译水平么?简直是侮辱高中生。我真的很怀疑这本书的译者怎么有胆量把自己的名字印在书上的,不嫌丢人么?我真的很...
评分昨天在图书馆翻了翻"时间序列分析"的书,发现这东西还是很有用的,利用时间作为自变量来预测一个时间序列未来的值,比如,可以预测地震、天气、股票等等,由于它的自变量只有时间,所以感觉很神奇,几乎就是拿一个变量自己来做回归,称之为自回归AR(auto regression),另...
浅显易懂,适合像我这样上过线代但是需要重新捡起来的人阅读,里面给的应用也比较切合实际,Numerical Note还可以在算法上提供一些建议。比国内的教材好的不是一星半点
评分不多的完整读完的几本数学书,在第一章就讲到了线性变换,附以图形和解释,也不会让人觉得太唐突,确实厉害。老外的书以书中图形多少作为书的一个亮点,国人为什么做不到呢?
评分浅显易懂,适合像我这样上过线代但是需要重新捡起来的人阅读,里面给的应用也比较切合实际,Numerical Note还可以在算法上提供一些建议。比国内的教材好的不是一星半点
评分线代基础
评分大爱~~~~写的相当相当好!
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