CLRS for Instructor 《算法导论——教师用书》（有部分习题答案） Computer Algorithms - Introduction to Design and Analysis 3rd - Sara Baase Solutions for CLRS (算法导论部分习题解答)

Özdemir, Caner著，逆合成孔径雷达成像的经典基础书藉，基本涵盖了ISAR成像的所有基本问题，英文原版，语言讲解易于理解，配有仿真图以及仿真代码，非常难得。

Numerical Algorithms Methods for Computer Vision Machine Learning and Graphics 英文无水印pdf pdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除

Introduction to the Design and Analysis of Algorithms 3rd Edition （算法分析设计基础Anany Levitin ）第三版课后答案，第三版，1-12章全，有hints也有solutions，英文版

Ernest P. Chan 的三部曲的第三部。 Machine Trading is a practical guide to building your algorithmic trading business. Written by a recognized trader with major institution expertise, this book provides step-by-step instruction on quantitative trading and the latest technologies available even outside the Wall Street sphere. You'll discover the latest platforms that are becoming increasingly easy to use, gain access to new markets, and learn new quantitative strategies that are applicable to stocks, options, futures, currencies, and even bitcoins. The companion website provides downloadable software codes, and you'll learn to design your own proprietary tools using MATLAB. The author's experiences provide deep insight into both the business and human side of systematic trading and money management, and his evolution from proprietary trader to fund manager contains valuable lessons for investors at any level.

Introduction_to_Algorithms(chinese)现代计算机常用数据结构和算法_南京大学

SPSS 25 演算法（SPSS_Statistics_Algorithms）IBM官方说明手册，繁体中文版。

计算机视觉算法与应用

raywenderlich 的 Data Structures and Algorithms in Swift pdf 格式。 更新到最新的 swift 4 版本。

最大流/最小割算法的简介，理解常用最大流最小割概念的文献，值得学习。 minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style “push-relabel” methods and algorithms based on Ford- Fulkerson style “augmenting paths.” We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.