Delphi下用Intraweb开发WEB程序应用实战(第二版)

说明：  本书包含数据结构和算法的基本概念及数学和 C#的知 识，常用的数据结构以及在.NET 框架中相应的数据结构查找常用的各种方法及其应用以及在.NET 框架中相应的算法。(This book contains the basic concepts of data structure and algorithm, the knowledge of mathematics and C , the common data structure, the common methods and applications for finding the corresponding data structure in the Net framework, and the corresponding algorithm in the Net framework.)

Lex yac usage and programming guide

说明：  DEA solver for windows 10 in matlab666

说明：  无线通信 中文版 田斌译 电子工业出版社(Chinese version of wireless communication Tian Bin Translation Electronic Industry Publishing House)

说明：  一本学习matlab的好书，附带有例子程序，共同分享(a learning Matlab books, fringe is an example procedure, shared)

说明：  本书共有43章，内容涵盖常见的神经网络（BP、RBF、SOM、Hopfield、Elman、LVQ、Kohonen、GRNN、NARX等）以及相关智能算法（SVM、决策树、随机森林、极限学习机等）。同时，部分章节也涉及了常见的优化算法（遗传算法、蚁群算法等）与神经网络的结合问题。此外，本书还介绍了MATLAB R2012b中神经网络工具箱的新增功能与特性，如神经网络并行计算、定制神经网络、神经网络高效编程等(This book consists of 43 chapters, covering common neural networks (BP, RBF, SOM, Hopfield, Elman, LVQ, Kohonen, GRNN, NARX, etc.) and related intelligent algorithms (SVM, decision tree, random forest, extreme learning machine, etc.). At the same time, some chapters also involve the combination of common optimization algorithms (genetic algorithm, ant colony algorithm, etc.) and neural networks. In addition, the book also introduces the new functions and characteristics of the neural network toolbox in MATLAB R2012b, such as parallel computing of neural networks, customized neural networks, efficient programming of neural networks, etc)

C#从入门到实践（全23章），适合初学者快速了解。(C# from entry to practice (all 23 chapters))

说明：  latex入门项目,学习如何用latex写论文(This is a startup project for latex.)

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.)

齐全的Window API,VC、VB、CB等都可用到的。(complete Window API, VC, VB, CB so available to.)