lyhkk's repositories
academicpages.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
ai-edu
AI education materials for Chinese students, teachers and IT professionals.
awesome-chatgpt-prompts
This repo includes ChatGPT prompt curation to use ChatGPT better.
awesome-courses
:books: List of awesome university courses for learning Computer Science!
awesome-spider
爬虫集合
BasicSR
Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet.
build-your-own-x
Master programming by recreating your favorite technologies from scratch.
bus-dispatching
OJ version
chatgpt-web
使用 express 和 vue3 搭建的 ChartGPT 演示网页
ChatPaper
Use ChatGPT to summarize the arXiv papers.
DeepLearning-MuLi-Notes
Notes about courses Dive into Deep Learning by Mu Li
Deformable-Convolution-V2-PyTorch
Deformable ConvNets V2 (DCNv2) in PyTorch
free-programming-books
:books: Freely available programming books
HelloQtNetwork
Show the use of Qt network module. 展示Qt网络模块的使用
image-classification
Tutorials on how to implement a few key architectures for image classification using PyTorch and TorchVision.
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，分布式爬虫，爬虫项目实战实例等
lhy_2021_Notes
深度学习 李宏毅 2021 学习笔记
PyTorch
【PyTorch】教学代码
reference
为开发人员分享快速参考备忘清单(速查表)
segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
SMFN_Revision
Based on SMFN_For_360VSR @lovepiano,add some new work
SR3--Diffusion-Model
Unofficial implementation of Image Super-Resolution via Iterative Refinement by Pytorch
Tuning-playbook-about-Deep-learning
A playbook for systematically maximizing the performance of deep learning models.