简单AES类,实现AES加密/解密,需要pycrypto支持.

Python和LDAP域认证,python-LDAP软件，以及python-LDAP资料，还包含有在python中的cookie处理。

2D开发游戏，简洁快速，用python来开放游戏把~~~~~~~~~~~~~~~~~~~~~

Python 安全编程教程

problem-solving-with-algorithms-and-data-structure-using-python 中文版

Python中文资料大集合
都郁闷死了 连续上传三次了，每次上传到百分之七八十就不行了。。。
简单介绍一下哈：
python入门,python程序员指南,python2.5官方指南，简明python教程,python学习笔记,python教学文件，Dive.Into.Python-zh-cn-5.4-with-code.chm
等等~~~

Python电影推荐系统+爬虫+可视化（协同过滤推荐算法）（包含项目源码+数据库文件+文档）计算机毕业设计 项目结构说明 |-- 项目 |-- db.sqlite3 数据库相关 重要 想看数据，可以用navicat打开 |-- requirements.txt 项目依赖库，可以理解为部分技术栈之类的 |-- 运行说明.txt 如何运行 |-- app 主要代码文件夹 | |-- models.py django的model 不懂百度一下即可 这个有点重要 | |-- views.py 后端主要代码 重点 重点 重点 重点 重点 重点 |-- meteorological | |-- settings.py 配置文件 | |-- urls.py 路由 这个有点重要 |-- static 静态文件夹 js css img这些文件 |-- templates 模板

python声音模仿训练模型包encoder.pt，synthesizer.pt，vocoder.pt，完整插入RTVC声音克隆模型，完整资源，不在需要谷歌云端下载

========dgk_lost_conv======== chinese conversation corpus 可以用作聊天机器人的训练语料 结果： dgk_shooter_z.conv 110MB 已分词 dgk_shooter_min.conv 按字分词 lost.conv 1.7MB fanzxl.conv 2.3MB fk24.conv 4.5MB haosys.conv 1.3MB juemds.conv 793KB laoyj.conv 1.5MB prisonb.conv 543KB 内部方法： asstosrt -s utf-8 ass ----asstosrt---->srt srt ----cvgen.py---->.conv 特别的shooter73g: 进入shooterwp， 解压缩mirror.x到rawbase下面 执行sel.sh 在跟目录下 fixco

Bayesian statistics has been around for more than 250 years now. During this time it has enjoyed as much recognition and appreciation as disdain and contempt. Through the last few decades it has gained more and more attention from people in statistics and almost all other sciences, engineering, and even outside the walls of the academic world. This revival has been possible due to theoretical and computational developments. Modern Bayesian statistics is mostly computational statistics. The necessity for exible and transparent models and a more interpretation of statistical analysis has only contributed to the trend. Here, we will adopt a pragmatic approach to Bayesian statistics and we will not care too much about other statistical paradigms and their relationship to Bayesian statistics. The aim of this book is to learn about Bayesian data analysis with the help of Python. Philosophical discussions are interesting but they have already been undertaken elsewhere in a richer way than we can discuss in these pages. We will take a modeling approach to statistics, we will learn to think in terms of probabilistic models, and apply Bayes' theorem to derive the logical consequences of our models and data. The approach will also be computational; models will be coded using PyMC3—a great library for Bayesian statistics that hides most of the mathematical details and computations from the user. Bayesian methods are theoretically grounded in probability theory and hence it's no wonder that many books about Bayesian statistics are full of mathematical formulas requiring a certain level of mathematical sophistication. Learning the mathematical foundations of statistics could certainly help you build better models and gain intuition about problems, models, and results. Nevertheless, libraries, such as PyMC3 allow us to learn and do Bayesian statistics with only a modest mathematical knowledge, as you will be able to verify by yourself throughout this book.