HMM用于词性标记

说明：  使用树莓派3b来实现无人驾驶汽车，使用ROS把各种传感器连接起来。(Using raspberry pie 3b to realize driverless car,and use ROS to connect all kinds of sensors.)

针对python的congfigparse功能，进行编程，并用实例验证方法的正确性。(The congfigparse for python functions to be programmed, and the correctness of the authentication method with an example.)

说明：  简单KNN算法编写，注释写的很详细，简单易懂，(simple KNN algorithm, it has been interpreted in detail)

基于python语言的适用于移动端应用的开源人脸识别算法mobilefacenet算法(Open source face recognition algorithm for mobile applications based on Python language)

说明：  30个常用Python程序实现，包括冒泡算法之类(30 commonly used Python programs, including bubbling algorithms and the like)

SSD图像识别，基于keras平台，识别速度的识别精度都还可以。(SSD image recognition, based on keras platform, the recognition accuracy of recognition speed is also acceptable.)

说明：  本书是经典畅销图书《Python 核心编程（第二版）》的全新升级版本，总共分为 3 部分。 第 1 部分讲解了 Python 的一些通用应用，包括正则表达式、网络编程、Internet 客户端编程、多线程编程、GUI 编程、数据库编程、Microsoft Office 编程、扩展 Python 等内容。 第 2 部分 讲解了与 Web 开发相关的主题，包括 Web 客户端和服务器、CGI 和 WSGI 相关的 Web 编程、Diango Web 框架、云计算、高级 Web 服务。第 3 部分则为一个补充/实验章节，包括文本处理以及一些其他内容。 本书适合具有一定经验的 Python 开发人员阅读(This book is a new upgraded version of the classic best-selling book Python Core Programming (2nd Edition). It is divided into three parts. Part 1 explains some general applications of Python, including regular expressions, network programming, Internet client programming, multi-threading programming, GUI programming, database programming, Microsoft Office programming, extension of Python and so on. Part 2 Topics related to Web development are explained, including Web client and server, CGI and WSGI related Web programming, Diango Web framework, cloud computing, advanced Web services. Part 3 is a supplementary/experimental chapter, including text processing and some other contents. This book is suitable for Python developers with some experience.)

模糊C均值聚类算法的PYTHON实现，在UCI的IRIS数据集上实现(Fuzzy C-means clustering algorithm PYTHON realization, implemented on UCI s IRIS data set)

RNN神经网络的应用和概念，RNN源代码和使用方法(You can find that the parameters `(W, U, V)` are shared in different time steps. And the output in each time step can be**softmax**. So you can use**cross entropy** loss as an error function and use some optimizing method (e.g. gradient descent) to calculate the optimized parameters `(W, U, V)`. Let recap the equations of our RNN: )