数值线性代数领域的经典之作

线性代数及其应用中文版(美)David CLay2005Part1

I developed this textbook while teaching the course Statistics for Financial Engineering to master’s students in the financial engineering program at Cornell University. These students have already taken courses in portfolio management, fixed income securities, options, and stochastic calculus, so I concentrate on teaching statistics, data analysis, and the use of R, and I cover most sections of Chaps. 4–12 and 18–20. These chapters alone are more than enough to fill a one-semester course. I do not cover regression (Chaps. 9–11 and 21) or the more advanced time series topics in Chap. 13, since these topics are covered in other courses. In the past, I have not covered cointegration (Chap. 15), but I will in the future. The master’s students spend much of the third semester working on projects with investment banks or hedge funds. As a faculty adviser for several projects, I have seen the importance of cointegration.

This book does not teach R programming, but each chapter has an “R lab” with data analysis and simulations. Students can learn R from these labs and by using R’s help or the manual An Introduction to R (available at the CRAN web site and R’s online help) to learn more about the functions used in the labs. Also, the text does indicate which R functions are used in the examples. Occasionally, R code is given to illustrate some process, for example, in Chap. 16 finding the tangency portfolio by quadratic programming. For readers wishing to use R, the bibliographical notes at the end of each chapter mention books that cover R programming and the book’s web site contains examples of the R and WinBUGS code used to produce this book. Students enter my course Statistics for Financial Engineering with quite disparate knowledge of R. Some are very accomplished R programmers, while others have no experience with R, although all have experience with some programming language. Students with no previous experience with R generally need assistance from the instructor to get started on the R labs. Readers using this book for self-study should learn R first before attempting the R labs.

The Basic Practice of Statistics is based on three principles: balanced content, experience with data, and the importance of ideas. These principles are widely accepted by statisticians concerned about teaching and are directly connected to and refl ected by the themes of the College Report of the Guidelines in Assessment and Instruction for Statistics Education (GAISE) Project.

微波工程 是 射频工程师 的入门读物 全书思路清晰覆盖范围广

破译者（英文版 The CodeBreakers David Kahn）这个可谓是经典了，文字版，还不错

David I. Schneider - Instructor Solutions Manual for An Introduction to Programming Using Python (2016, Pearson).pdf

语义检索必备算法，很好用的一本书。如果你在做柔性查询、语义处理等可下载使用。

OSCP 我准备OSCP的资料库-感谢CODE SPACE，尤其是我的老师David Reguera（DREG）和Enrique Lauroba。 在该存储库中，我将添加OSCP准备工作中的帖子和文件。 我正在努力提高英语水平，因此我的写作中可能有错误。 任何更正或改进将受到欢迎。 另一方面，我将用西班牙语和英语撰写文章。 根据渴望和我找到自己的那一刻。 我希望你喜欢它