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python-reference

Python Quick Reference

python-sugar

As my knowledge in Python and Spark progresses, I document what I have learnt.

python-systemd-tutorial

A tutorial for writing a systemd service in Python

python-telegram-bot

We have made you a wrapper you can't refuse

python-to-baidumap

基于Python3实现百度地图POI检索数据获取，并写入json与exce文件。*注意* 百度最多返回400条记录

python-toolbox-for-rapid

a Python toolbox for the RAPID (Routing Application for Parallel computatIon of Discharge) model.

python-uncompyle6

A Python cross-version decompiler

python-visualization-landscape

Adaptation of Jake VanderPlas graphic about python visualization landscape

python-webshell

webshell writen in python

python3webspider

Source File of My Book related to WebSpider

python_geospatial

Geospatial Data in Python Tutorial Materials

python_interview_question

关于python的面试题

python_learning_notes

《learning python》中文第四版的学习笔记，由纸值文档转换而来

python_reference

Useful functions, tutorials, and other Python-related things

python_runstats

Python module for computing statistics and regression in a single pass.

python_tour_of_data_science

A Python Tour of Data Science

pythondata

pythondatasciencehandbook

Jupyter Notebooks for the Python Data Science Handbook

pythondemo

虾神的Python示例代码库

pytips

Useful Python tips!

pytorch-book

PyTorch：入门与实践

pytorch-cheatsheet

Getting things done with Pytorch

pytorch-cn

Pythrch-CN文档地址

pytorch-deep-learning-minicourse

Minicourse in Deep Learning with PyTorch

pytorch-handbook

pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行

pytorch-made

MADE (Masked Autoencoder Density Estimation) implementation in PyTorch

pytorch-tensor-decompositions

PyTorch implementation of [1412.6553] and [1511.06530] tensor decomposition methods for convolutional layers.

pytorch-yolo3

pytorch yolo3

pytorch_mask_rcnn

Converted from [tf+keras version MASK-RCNN](https://github.com/matterport/Mask_RCNN)

pytorch_notebooks

🔥A collection of PyTorch notebooks for learning and practicing deep learning