可爱的Python(高清中文版)

说明：  使用python进行信号EMD分析，然后LSTM输入信号特征，然后分类(The signal EMD is analyzed by python, then LSTM input signal characteristics are classified.)

由于Relief算法比较简单，运行效率高，并且结果也比较令人满意，因此得到广泛应用，但是其局限性在于只能处理两类别数据，因此1994年Kononeill对其进行了扩展，得到了ReliefF作算法，可以处理多类别问题。该算法用于处理目标属性为连续值的回归问题。(Because Relief algorithm is relatively simple, high efficiency, and the results are more satisfactory, so widely used, but its limitation is that only two types of data can be processed, so Kononeill in 1994 to expand it, get ReliefF algorithm , Can handle multiple categories of problems. The algorithm is used to deal with the regression problem that the target attribute is a continuous value.)

Ce projet a pour but de nous introduire d’une manière pratique aux techniques de traitement automatique du langage naturel

说明：  学习资料，仅供参考，好好学习，多多益善，日积月累，逆水行舟，能者上。(Learning materials, for reference only, study hard, more good, accumulated over time, sailing against the current, capable people.)

【实例简介】一个简单的博客项目

说明：  以图像处理和knn算法为基础，利用knn算法，在python开发环境中设计了一个手写数字识别系统，该系统采用28*28的模板对图新分成784块提取手写数字的784个特征，在二值图像数据基础上，通过朴素贝叶斯分类器算法，对0~9这十类手写数字进行学习和分类。(Based on image processing and KNN algorithm, a handwritten digit recognition system is designed in Python development environment by using KNN algorithm. The system uses 28*28 template to divide the graph into 784 blocks to extract 784 features of handwritten digits. On the basis of binary image data, it learns and classifies the ten types of handwritten digits, 0-9, through Naive Bayesian classifier algorithm.)

用python所编写的Rtree的实例，实现Rtree的创建，查询，删除(Examples python with written Rtree achieve Rtree create, query, delete)

说明：  　其中x是任何一个大于xmin的数，xmin是X最小的可能值（正数），k是为正的参数。帕累托分布曲线族是由两个数量参数化的：xmin和k。(Where x is any number greater than xmin,xmin is the smallest possible value of x (positive), and k is a positive parameter the Pareto family of distribution curves is parameterized by two quantities :xmin and K)

mbedOS开发工具cli线下编译平台，基于python2.7(MbedOS development tool cli offline compiler platform, based on python2.7)

The elegant and special HW for signal processing, GSD part