Object Oriented Programming-An Evolutionary Approach

快速NLfea 专为拓扑优化量身定制的快速高效的Matlab代码，用于几何非线性有限元分析。 要求 Matlab 2020b或更高版本 来自Stefan fsparse的Matlab库 当前能力 此代码使用线性材料模型解决了快速有效的几何非线性FEA问题。 fasNLfea已通过具有预定义非线性行为的拓扑优化成功应用于初始机翼设计。 该代码是为拓扑优化而量身定制的。 假定尺寸为nelx*nely*nelz的均匀网格。 每个元素的密度为xPhys 。 此密度描述了域的拓扑。 单元刚度由SIMP材料模型确定。 能量插值方案可降低低刚度/空隙元素的网格变形。 fasNLfea是研究人员和学生进行几何非线性拓扑优化的一个很好的起点。 它的计算效率使您可以在数小时内（而不是数天）内解决拓扑优化问题。 未来功能/发展 缩短计算时间 3维网格的测试 添加和发布验证（仅针对论文中的2维案例验证） 添

Elements of Programming Interviews: The Insiders' Guide的题目和code都很赞，觉得是目前准备coding interview最好的书了，超全面超详细，有不少leetcode的题解。 书名：Elements of Programming Interviews: The Insiders' Guide 副标题: 300 Questions and Solutions 作者：by Adnan Aziz, Tsung-Hsien Lee , Amit Prakash 出版时间：October 11, 2012 (1st edition) 页数: 506 语言: English 定价: USD 39.95 ISBN-10: 1479274836 ISBN-13: 978-1479274833 出版社: CreateSpace Independent Publishing Platform 相关链接： https://github.com/imath66/Elements-of-Programming-Interviews-Java-Solution

统计学习数据挖掘推理和预测的要素 This is page v Printer: Opaque this To our parents: Valerie and Patrick Hastie Vera and Sami Tibshirani Florence and Harry Friedman and to our families: Samantha, Timothy, and Lynda Charlie, Ryan, Julie, and Cheryl Melanie, Dora, Monika, and Ildiko vi This is page vii Printer: Opaque this Preface to the Second Edition In God we trust, all others bring data. –William Edwards Deming (1900-1993)1 We have been gratified by the popularity of the first edition of The Elements of Statistical Learning. This, along with the fast pace of research in the statistical learning field, motivated us to update our book with a second edition. We have added four new chapters and updated some of the existing chapters. Because many readers are familiar with the layout of the first edition, we have tried to change it as little as possible. Here is a summary of the main changes: 1On the Web, this quote has been widely attributed to both Deming and Robert W. Hayden; however Professor Hayden told us that he can claim no credit for this quote, and ironically we could find no “data” confirming that Deming actually said this. viii Preface to the Second Edition Chapter What’s new 1. Introduction 2. Overview of Supervised Learning 3. Linear Methods for Regression LAR algorithm and generalizations of the lasso 4. Linear Methods for Classification Lasso path for logistic regression 5. Basis Expansions and Regulariza- Additional illustrations of RKHS tion 6. Kern

有限元编程较好的资料，内容不多，一会儿功夫就可以看完，但是实用

Elements of Programming provides a different understanding of programming than is presented elsewhere. Its major premise is that practical programming, like other areas of science and engineering,must be based on a solid mathematical foundation. The book shows that algorithms implemented in a real programming language, such as C++, can operate in the most general mathematical setting. For example, the fast exponentiation algorithm is defined to work with any associative operation. Using abstract algorithms leads to efficient, r eliable, secure, and economical software. This is not an easy book. Nor is it a compilation of tips and tricks for incremental improvements in your programming skills. The book’s value is more fundamental and, ultimately, more critical for insight into programming. To benefit fully, you will need to work through it from beginning to end, reading the code, proving the lemmas, and doing the exercises. When finished, you will see how the application of the deductive method to your programs assures that your system’s software components will work together and behave as they must. The book presents a number of algorithms and requirements for types on which they are defined. The code for these descriptions—also available on the Web—is written in a small subset of C++ meant to be accessible to any experienced programmer. This subset is defined in a special language appendix coauthored by Sean Parent and Bjarne Stroustrup. Whether you are a software developer, or any other professional for whom programming is an important activity, or a committed student, you will come to understand what the book’s experienced authors have been teaching and demonstrating for years—that mathematics is good for programming, and that theory is good for practice.

The proliferation of information housed in computerized domains makes it vital to find tools to search these resources efficiently and effectively. Ordinary retrieval techniques are inadequate because sorting is simply impossible. Consequently, proximity searching has become a fundamental computation task in a variety of application areas. Similarity Search focuses on the state of the art in developing index structures for searching the metric space. Part I of the text describes major theoretical principles, and provides an extensive survey of specific techniques for a large range of applications. Part II concentrates on approaches particularly designed for searching in large collections of data. After describing the most popular centralized disk-based metric indexes, approximation techniques are presented as a way to significantly speed up search time at the cost of some imprecision in query results. Finally, the scalable and distributed metric structures are discussed.

Daniel A. Spielman Dept. of Computer Science Program in Applied Mathematics Yale Unviersity

计算机视觉类书，清华大学出版社

解析深度学习-语音识别实践-英文版，没有找到中文版的