基于socket和tkinter的python网络聊天室程序

说明：  针对图像亮度不均匀问题，Li Tao与2005年提出的这个算法。其主要用于提升再低照度或者不均匀光照条件下拍摄到的图像亮度。该算法主要由两部分构成：自适应亮度增强模块和自适应对比度增强。(Li Tao and this algorithm proposed in 2005 for the problem of uneven image brightness. It is mainly used to enhance the brightness of images captured under low illumination or uneven illumination. The algorithm is mainly composed of two parts: adaptive brightness enhancement module and adaptive contrast enhancement.)

说明：  针对早期windows系统SMB协议的攻击 前提时先安装安装python 在命令提示符窗口中首先进入py命令所在的文件夹 直接键入python xx.py后回车确认(Attacks on SMB Protocol of Early Windows System Install Python first First enter the folder where the PY command resides in the command prompt window Type Python xx.py directly and return to confirm)

说明：  WGAN生成对抗网络代码实例，为下载者提供一种思路(WGAN generates adversarial network code, provides an idea)

神经网络，全连接层

说明：  主成分分析图像，分析图像像素组成部分，高维提取低维特征(Principal component analysis image, analysis of image pixel components, high dimensional extraction of low dimensional features)

说明：  使用双向LSTM神经网络预测时间序列，70%的训练集(Bidirectional LSTM neural networks are used to predict time series)

本书由 Immunity 公司的高级安全研究员 Justin Seitz 精心撰写。作者根据自己在安全界，特别是渗透测试领域的几十年经验，向读者介绍了 Python 如何被用在黑客和渗透测试的各个领域，从基本的网络扫描到数据包捕获，从 Web 爬虫到编写 Burp 扩展工具，从编写木马到权限提升等。(The book is written by Justin Seitz, a senior security researcher at Immunity company. Based on his decades of experience in the security community, especially in the field of penetration testing, the author introduces how Python is used in various fields of hacker and penetration testing, from basic network scanning to packet capture, from Web crawler to Burp extension tool, from writing Trojan to privilege escalation, and so on.)

主成分分析图像，分析图像像素组成部分，高维提取低维特征(Principal component analysis image, analysis of image pixel components, high dimensional extraction of low dimensional features)

说明：  多标签学习算法的相关资料学习，包含数据集和代码(the multi-label knn algorithm ,can use the code in python)

通过各种不同的算法实现室内高精度定位，比如说KNN，随机森林，SVM，等等。(Indoor high-precision positioning is achieved by various algorithms, such as KNN, random forest, SVM, and so on.)