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介绍如何使用 Macromedia Dreamweaver MX 2004，其对象是对该软件的主要特 性尚不熟悉的用户。本指南中的教程将引导您完成创建一个简单但具备一定功能的 Web 站点的过程(How to use Macromedia Dreamweaver MX 2004, its object is the main features of the software is not yet familiar with the user. Tutorial in this guide will walk you through creating a simple but functional Web site with a certain process)

rn的开发学习资料，英文版，初级RN学习(RN learning materials)

Python配套使用资料，更直观的看到协议分析图，更好地学习。(Python supporting the use of data, see the protocol analysis more intuitive, better learning. )

iSIGHT集成ANSYS手机实例。 掌握isight如何过程集成商业软件ANSYS。 了解iSIGHT提供的优化，试验设计，回归建模这三种确定性方法。()

三菱CCLINK教程PPT，主要用于进行通信设置，程序编程讲解(Mitsubishi CCLINK tutorial PPT, mainly used for communication settings, programming explained)

说明：  厚黑学,一本非常好的书。对于您的成功将起来很大的帮助(Thick black school, a very good book. For your success will be of great help to them)

《TCP/IP详解，卷1：协议》是一本完整而详细的TCP/IP协议指南。描述了属于每一层的各个协议以及它们如何在不同操作系统中运行。作者用Lawrence Berkeley实验室的tcpdump程序来捕获不同操作系统和TCP/IP实现之间传输的不同分组。对tcpdump输出的研究可以帮助理解不同协议如何工作。 本书适合作为计算机专业学生学习网络的教材和教师参考书。也适用于研究网络的技术人员。 (" TCP/IP Detailed, Volume 1: The agreement" is a complete and detailed TCP/IP protocol guidelines. Describe each level belonging to the various agreements and how they run on different operating systems. Author Lawrence Berkeley Laboratory with the tcpdump program to capture the different operating systems and TCP/IP to achieve the different between the sub-transmission. Tcpdump output of research can help understand how different protocols work. This book suitable for students learning computer network reference materials and teachers. Research network also applies to technical staff.)

说明：  Lua程序设计（第2版）中文，电子工业出版社出版。(Teaching lua programming)

关于西门子s7—200系列的课件，主要介绍了他的编程及工业中的应用(About Siemens s7-200 series of courseware, the main introduction of his programming and Industrial Application)

说明：  统计学习是计算机及其应用领域的一门重要的学科本书全面系统地介绍了统计学习的主要方法，特别是监督学习方法，包括感知机、K近邻法、朴素贝叶斯法、决策树、逻辑斯谛回归、与最大熵模型、支持向量机、提升方法、EM算法、隐马尔可夫模型和条件随机场等。(Statistical learning is an important subject in the field of computer and application of this book comprehensively systematically introduces the main methods of statistical learning, especially the supervision method of study, including the perception, K neighbor method, naive bayesian method, decision tree, logistic regression, and the maximum entropy model, support vector machine (SVM), promotion methods, the EM algorithm, the hidden markov model and conditional random field, etc.)