chronuxintroduction

这个非常适合有初步学习经验的人来更加深入学习matlab编程，其中有实际的例子非常适合学习(This is very suitable for people with preliminary learning experience to learn more about matlab programming, and there are practical examples that are very suitable for learning.)

C++ Primer, Fourth Edition (C++ Primer, Fourth Edition)

说明：  逆合成孔径雷达成像（附MATLAB源代码） Inverse Synthetic Aperture Radar Imaging__ With MATLAB Algorithms 作者CANER ?ZDEMIR, PhD 本书提供了充分的代表性逆合成孔径雷达（ISAR）成像，并给出了文中具体算法的仿真程序。(Inverse Synthetic Aperture Radar Imaging With MATLAB Algorithms By CANER OZDEMIR PhD)

MATLAB+R2014a完全自学一本通，电子书籍，完整的一本书，可以从其中学习matlab详细的知识(MATLAB+R2014aCompletely self-taught)

delphi学习(delphi study)

说明：  介绍算法的书及资料，有要学习算法的宝宝可以看看。(If you want to learn programming ,you can read this book.)

说明：  80x86汇编讲解，主要针对保护模式编程序，对搞汇编的是一个提高(80x86 on the compilation, mainly aimed at protection mode programmed to engage in the compilation is a raise)

电子工业出版社.无线通信系统仿真--C++实用模型(Electronics Industry Publishing House. Wireless communication system simulation- C++ Utility Model)

说明：  verilog 2005 IEEE 标准手册(IEEE Standard for Verilog 2005 Hardware Description Language)

说明：  Python数据挖掘入门与实践 本书作为数据挖掘入门读物，介绍了数据挖掘的基础知识、基本工具和实践方法，通过循序渐进地讲解算法，带你轻松踏上数据挖掘之旅。本书采用理论与实践相结合的方式，呈现了如何使用决策树和随机森林算法预测美国职业篮球联赛比赛结果，如何使用亲和性分析方法推荐电影，如何使用朴素贝叶斯算法进行社会媒体挖掘，等等。本书也涉及神经网络、深度学习、大数据处理等内容。本书面向愿意学习和尝试数据挖掘的程序员。(Introduction and practice of Python data mining As a primer on data mining, this book introduces the basic knowledge, basic tools and practical methods of data mining. By step-by-step explanation of the algorithm, you can easily embark on the journey of data mining. This book uses a combination of theory and practice, showing how to use the decision tree and random forest algorithm to predict the results of the American professional basketball league game, how to use the affinity analysis method to recommend movies, how to use the naive Bayesian algorithm for social media mining ,and many more. This book also covers neural networks, deep learning, big data processing and more. This book is for programmers who are willing to learn and experiment with data mining.)