python ntpserver

说明：  数据清洗 特征选择 通过python对数据进行处理(Data cleaning feature selection by python to deal with data)

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：  图像超分辨率技术源码； 基于深度学习的图像超分辨率重建的流程如下[2]： 1.首先找到一组原始图像Image1; 2.将这组图片降低分辨率为一组图像Image2 3.通过各种神经网络结构，将Image2超分辨率重建为Image3(Image3和Image1分辨率一样) 4.通过PSNR等方法比较Image1和Image3，验证超分辨率重建的效果，根据效果调节神经网络中的节点和参数 5.反复执行以上直到第4步的结果比较满意(Image super-resolution technology source code; The process of image super-resolution reconstruction based on deep learning is as follows [2]: 1. First find a set of original images image1; 2. Reduce the resolution of this group of pictures to a group of image2 3. Through various neural network structures, the super-resolution of image2 is reconstructed into image3 (the resolution of image3 is the same as that of image1) 4. Compare image1 and image3 by PSNR and other methods to verify the effect of super-resolution reconstruction, and adjust the nodes and parameters of neural network according to the effect 5. Repeat the above until the result of step 4 is satisfactory)

说明：  维尼通编程例程，简单易学，容易上手，希望对你有所帮助(Veneton Programming Routine)

说明：  用python语言编写的粒子群优化算法，内有多种适应度函数可供选择(Python language used particle swarm optimization algorithm, there are a variety of alternative fitness function)

 马尔科夫决策过程(Markov Decision Process)是强化学习(reinforcement learning)最基本的模型框架。它对序列化的决策过程做了很多限制。比如状态StSt和动作atat只有有限个、 请点击左侧文件开始预览 ！预览只提供20%的代码片段，完整代码需下载后查看 加载中 侵权举报

训练一个深度学习的循环神经网络，可实现获取公开数据集并进行训练，框架为Pytorch(Training a deep learning circular neural network can achieve the acquisition and training of open data sets, and the framework is Pytorch)

说明：  这个文件中写了对于一个德国医药品牌在中国销售额基于时间的预测，运用了机器学习算法。(This document describes a time-based forecast of sales of a German pharmaceutical brand in China, using machine learning algorithms.)

说明：  用于网页大麦网抢票脚本，用于演唱会门票，剧院门票等(damaiwang Concert ticket)

说明：  使用蛙跳算法来解决一个工厂流程问题的事例(Frog hopping algorithm solves factory process problems)