tkh666 Goto Github PK

a-classifier-with-pytorch

ailearning

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

android_kernel_msm-5.10_oneplus_sm8450

assignment

各种书面作业

bert_demo_mnn

cell-stem-cell-isthmus-stem-cells

chatgpt-next-web-1

One-Click to deploy well-designed ChatGPT web UI on Vercel. 一键拥有你自己的 ChatGPT 网页服务。

clc

CLC: A Consensus-based Label Correction Approach in Federated Learning

cmake-examples

Useful CMake Examples

co-teaching

NeurIPS'18: Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

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

copytranslator

Foreign language reading and translation assistant based on copy and translate.

course

高性能并行编程与优化 - 课件

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[email protected] 版权所有，违权必究 Tan 2018.06

end2end-federated-learning

A demo of end-to-end federated learning system.

facebook-scraper

Scrape Facebook public pages without an API key

facebook_crawler

fbcrawl

A Facebook crawler

fed-ncl

Federated Noisy Client Learning

feddyn

federated-learning

A PyTorch Implementation of Federated Learning http://doi.org/10.5281/zenodo.4321561

fedrolex

[NeurIPS 2022] "FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction" by Samiul Alam, Luyang Liu, Ming Yan, and Mi Zhang

framework-learning

计算机学习资料(Java , Jvm , Linux , Mysql , Netty , Redis , Netty , Spring , SpringBoot , Mybatis , Rabbitmq ,计算机网络 , 数据结构与算法 , 设计模式 )Github网页阅读:https://guang19.github.io/framework-learning , Gitee网页版阅读: https://qsjzwithguang19forever.gitee.io/framework-learning

fucking-algorithm

刷算法全靠套路，认准 labuladong 就够了！English version supported! Crack LeetCode, not only how, but also why.

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)。

heterofl-computation-and-communication-efficient-federated-learning-for-heterogeneous-clients

[ICLR 2021] HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients

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interview

Interview = 简历指南 + LeetCode + Kaggle

jd_kill

京东茅台懂得都懂 main分支是新版本，old分支是docker之前的版本

jd_seckill