1.用模拟退火方法解决Python程序中的车辆容量问题

说明：  利用python进行数据分析，英文书籍，从pandas库的数据分析工具开始利用高性能工具对数据进行加载、清理、转换、合并以及重塑；利用matpIotlib创建散点图以及静态或交互式的可视化结果；利用pandas的groupby功能对数据集进行切片、切块和汇总操作；处理各种各样的时间序列数据。(Starting from the data analysis tools of pandas database, high performance tools are used to load, clean, transform, merge and remodel data; scatter plots and static or interactive visualization results are created by matpIotlib; data sets are sliced, sliced and aggregated by pandas group by function; and various operations are processed. Time series data.)

说明：  采用生成对抗网络（GAN）实现图像去模糊，用于图像的清晰化处理(The generated countermeasure network (GAN) is used to remove the blur of the image, which is used to clear the image)

说明：  自己写的一个关于python的小型爬虫，性能感觉还算可以。(Himself wrote a small crawler about python, performance feel return calculate can.)

点云插值，离散点云插值，孔洞填充，使用python语言(Point cloud interpolation, discrete point cloud interpolation, hole filling, using Python language)

可以通过关键词搜索来对谷歌图片进行爬取，python脚本，自动保存，数据采集必备(You can crawl Google pictures by keyword search, python scripts, automatically save, data acquisition necessary)

在多晶体材料的计算中能够给多晶体赋予材料属性，然后利用晶体塑性模型计算(It can give material properties to polycrystals.)

学习python网路爬虫必备，对这方面学习非常有用。(Necessary for learning Python crawler)

说明：  渐进的让生成器和判别器增长：从一个低分辨率开始，随着训练发展，不断添加新层使模型增加更好的细节。(Let the generator and discriminator grow gradually: start with a low resolution and add new layers to the model with the development of training.)

说明：  该demo展示了使用opencv和face_recognition库，对图片中出现的人脸识别记录并框出的过程，可实际应用(This is a demo which implements face recognition with opencv and face_recognition libraries)

说明：  xgboot做预测示例的代码及数据集，仅作为参考使用(The code and data set of xgboot as a prediction example are used for reference only)