TKH666's repositories
Probabilistic-Programming-and-Bayesian-Methods-for-Hackers
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
fbcrawl
A Facebook crawler
PKUAutoElective
北大选课网补退选阶段自动选课小工具
Plate_recognize_AOLP
MTCNN detect the plates,split character by Vertical projection
DeepLearning-500-questions
深度学习500问,以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述,以帮助自己及有需要的读者。 全书分为18个章节,50余万字。由于水平有限,书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作,联系scutjy2015@163.com 版权所有,违权必究 Tan 2018.06
Interview
Interview = 简历指南 + LeetCode + Kaggle
PythonDataScienceHandbook
Python Data Science Handbook: full text in Jupyter Notebooks
user_restful_api
这是一个通用的用户、部门、角色、权限管理的API接口
sklearn-doc-zh
:book: [译] scikit-learn(sklearn) 中文文档
CopyTranslator
Foreign language reading and translation assistant based on copy and translate.
License_Plate_Detection_Pytorch
A two stage lightweight and high performance license plate recognition in MTCNN and LPRNet
AiLearning
AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP
learn_python3_spider
python爬虫教程系列、从0到1学习python爬虫,包括浏览器抓包,手机APP抓包,如 fiddler、mitmproxy,各种爬虫涉及的模块的使用,如:requests、beautifulSoup、selenium、appium、scrapy等,以及IP代理,验证码识别,Mysql,MongoDB数据库的python使用,多线程多进程爬虫的使用,css 爬虫加密逆向破解,JS爬虫逆向,爬虫项目实战实例等
co-trans-ext
Chrome系浏览器的一个集搜狗翻译、百度翻译、谷歌翻译、有道翻译、金山词霸于一体的翻译扩展
libpku
贵校课程资料民间整理
Python-crawler-tutorial-starts-from-zero
python爬虫教程,带你从零到一,包含js逆向,selenium, tesseract OCR识别,mongodb的使用,以及scrapy框架
machine-learning-notes
My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (1000+ slides) 我不间断更新的机器学习,概率模型和深度学习的讲义(1000+页)和视频链接
googletranslate.popclipext
(Unofficial) A free and easy-to-use Google Translate PopClip Extension (macOS). 一个免费的谷歌翻译 PopClip 插件 (macOS)。
assignment
各种书面作业
PKUGetClassHelper
A tool for students in Peking Univ. for their election of classes
StudyMaterials
建站入门学习资料,微信小程序入门初步
MMDS_Exercises
Solutions to the Exercises found in Mining Massive Datasets