曼博（Udi Manber）美国著名的计算机科学家，国际公认的算法大师，在线信息搜索引擎的先驱。1982年于华盛顿大学获得计算机科学博士学位，曾是美国亚利桑那大学计算机专业教授。。他提出的UDI测试已经成为衡量搜索引擎质量的评估标准。 本书着重于阐述构造算法的思想，而非算法本身的实现细节，比如基于归纳思想统一了分治和动态规划，通过引入增强归纳假设描述了动态规划的一般设计技巧。 本资源为DJVU格式，专为书本扫描优化，高清版

This book surveys computer algorithms and programming techniques for specifying and generating motion for graphical objects, that is, computer animation. It is primarily concerned with three-dimensional (3D) computer animation. The main audience is advanced undergraduate or beginning graduate students in Computer Science. Computer graphics programmers who want to learn the basics of computer animation programming and artists who use software packages to generate computer animation (digital animators) who want to better understand the underlying computational issues of animation software will also benefit from this book.

High Efficiency Video Coding (Hevc) Algorithms and Architectures;

【作　　者】Sedgewick, Robert [同作者作品] 【出 版 社】 Addison-Wesley 【书 号】 0201361183 【上架时间】 2008-3-31 【出版日期】 2005 年7月 Algorithms in C++, Parts 1–4: Fundamentals, Data Structure, Sorting, Searching, Third Edition By Robert Sedgewick - Princeton University ............................................... Publisher: Addison Wesley Professional Pub Date: July 13, 1998 Print ISBN-10: 0-201-35088-2 Print ISBN-13: 978-0-201-35088-3 Web ISBN-10: 0-7686-8533-8 Web ISBN-13: 978-0-7686-8533-6 Pages: 752 Slots: 2.0 Table of Contents | Index Robert Sedgewick has thoroughly rewritten and substantially expanded and updated his popular work to provide current and comprehensive coverage of important algorithms and data structures. Christopher Van Wyk and Sedgewick have developed new C++ implementations that both express the methods in a concise and direct manner, and also provide programmers with the practical means to test them on real applications. Many new algorithms are presented, and the explanations of each algorithm are much more detailed than in previous editions. A new text design and detailed, innovative figures, with accompanying commentary, greatly enhance the presentation. The third edition retains the successful blend of theory and practice that has made Sedgewick's work an invaluable resource for more than 250,000 programmers! This particular book, Parts 1n4, represents the essential first half of Sedgewick's complete work. It provides extensive coverage of fundamental data structures and algorithms for sorting, searching, and related applications. Although the substance of the book applies to programming in any language, the implementations by Van Wyk and Sedgewick also exploit the natural match between C++ classes and ADT implementations. Highlights Expanded coverage of arrays, linked lists, strings, trees, and other basic data structures Greater emphasis on abstract data types (ADTs), modular programming, object-oriented programming, and C++ classes than in

《游戏编程: 算法和技术》的英文原版，非扫描

Boosting, Foundations and Algorithms,Adaptive Computation and Machine Learning,The MIT Press

Sherwani寫的關於實體設計自動化的書...內涵豐富演算法...大家參考看看!

Learn how to implement the most common and useful data structures and algorithms in Swift! Currently updated to: Platform: iOS12; Language: Swift4.2; Editor: Xcode10

在很多网站上得知problem-solving-with-algorithms-and-data-structure-using-python是一本很好的书，此处只是中文版，英文版在：http://www.interactivepython.org/runestone/static/pythonds/index.html#。

Contents Preface xv Prologue: A machine learning sampler 1 1 The ingredients of machine learning 13 1.1 Tasks: the problems that can be solved with machine learning . . . . . . . 14 Looking for structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Evaluating performance on a task . . . . . . . . . . . . . . . . . . . . . . . . 18 1.2 Models: the output of machine learning . . . . . . . . . . . . . . . . . . . . 20 Geometric models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Probabilistic models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Logical models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Grouping and grading . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 1.3 Features: the workhorses of machine learning . . . . . . . . . . . . . . . . 38 Two uses of features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Feature construction and transformation . . . . . . . . . . . . . . . . . . . 41 Interaction between features . . . . . . . . . . . . . . . . . . . . . . . . . . 44 1.4 Summary and outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 What you’ll find in the rest of the book . . . . . . . . . . . . . . . . . . . . . 48 2 Binary classification and related tasks 49 2.1 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 ixx Contents Assessing classification performance . . . . . . . . . . . . . . . . . . . . . . 53 Visualising classification performance . . . . . . . . . . . . . . . . . . . . . 58 2.2 Scoring and ranking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Assessing and visualising ranking performance . . . . . . . . . . . . . . . . 63 Turning rankers into classifiers . . . . . . . . . . . . . . . . . . . . . . . . . 69 2.3 Class probability estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 Assessing class probability estimates . .