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说明：  python 高级数据处理，实现各种数据的处理，得出结果(Python advanced data processing, processing various data, and getting results)

V4、 V5，V6 KMS 请求的响应。-激活 Windows 的支持 7/8/8.1/2008R2/2012/2012R2 和 Office 2010/2013 年。 # 依赖项-Python 2.7.x 或者 Python 2.6.x 与安装的"argparse"模块。-如果安装"pytz"模块，在详细输出"请求时间"将转换为本地时间。否则，它将以 utc 时间表示。 # 用法-要启动服务器，执行 "python server.py [listen_address] [端口]"。默认侦听地址是"0.0.0.0"（所有的接口） 和默认端口是"1688"。-向运行客户端，使用 "python client.py server_address [端口]"。默认端口是"1688"。

说明：  Unet深度卷积网络道路提取，对比包括密度网络resnext和linknet。(unet for road extraction)

我们提供一类称为MobileNets的高效模型，用于移动和嵌入式视觉应用。?MobileNets是基于一个流线型的架构，它使用深度可分离的卷积来构建轻量级的深层神经网络。我们引入两个简单的全局超参数，在延迟度和准确度之间有效地进行平衡。这两个超参数允许模型构建者根据问题的约束条件，为其应用选择合适大小的模型。我们进行了资源和精度权衡的广泛实验，与ImageNet分类上的其他流行的网络模型相比，MobileNets表现出很强的性能。最后，我们展示了MobileNets在广泛的应用场景中的有效性，包括物体检测，细粒度分类，人脸属性和大规模地理定位。(We provide an efficient model called MobileNets for mobile and embedded vision applications. MobileNets is based on a streamlined architecture that USES deep separable convolution to build a lightweight deep neural network. We introduce two simple global hyperparameters to effectively balance the delay and accuracy. These two hyperparameters allow the model builder to select an appropriate size model for its application based on the constraints of the problem. We conducted extensive experiments on resource and precision tradeoffs, and MobileNets showed strong performance compared with other popular network models on the ImageNet classification. Finally, we demonstrate the effectiveness of MobileNets in a wide range of application scenarios, including object detection, fine-grained classification, face attributes, and large-scale geographic localization.)

说明：  使用Python-OpenCV实现的MSRCR算法，可以用于水下图像的复原(An algorithm base on Python-OpenCV. Using MSRCR measure which can be used for underwater image restoration.)

说明：  各种量化投资策略代码，各大量化投资平台内部资料，切勿外传 ！(Various quantitative investment strategy codes)

机器学习算法，无监督学习，利用k均值聚类算法对未标注数据分组(Machine learning algorithms, unsupervised learning, the use of k-means clustering algorithm for unlabeled data packets)

用python实现复杂网络的度分布绘图，本程序以wyp同志的论文数据为例。(Degree with a python distribution mapping of complex networks, the procedures to wyp comrades paper data as an example.)

说明：  此书是opencv的官网教程（中文版），针对Python的(This book is opencv's official website tutorial (Chinese version) for Python)

说明：  机器学习实战第三章编程实现：使用决策树预测隐形眼镜类型。(Machine Learning Practical Combat Chapter 3 Programming Realization)