maple123xx's Stars
Alinshans/MyTinySTL
Achieve a tiny STL in C++11
EZLippi/Tinyhttpd
Tinyhttpd 是J. David Blackstone在1999年写的一个不到 500 行的超轻量型 Http Server,用来学习非常不错,可以帮助我们真正理解服务器程序的本质。官网:http://tinyhttpd.sourceforge.net
MisterBooo/LeetCodeAnimation
Demonstrate all the questions on LeetCode in the form of animation.(用动画的形式呈现解LeetCode题目的思路)
linw7/Skill-Tree
🐼 准备秋招,欢迎来树上取果实
TheAlgorithms/Python
All Algorithms implemented in Python
YangKing0834131/2018-User-purchase-forecast
China Merchants Bank Credit Card Center Campus Competition: Prediction of User Purchases in the Consumer Finance Scenario A Ranking: rank2 B Ranking: rank3
chenkkkk/User-purchase-forecast
招商银行信用卡中心校园大赛:消费金融场景下的用户购买预测 Rank 3rd
yuebaixiao/cs_recipes
陈硕视频里的recipes-master
hanhao0125/movie_recommend
电影推荐,管理, apriori算法,svd算法
kaiya/Weekly_report
kaiya/federated-averaging-tutorials
A set of tutorials to implement the Federated Averaging algorithm on TensorFlow.
kaiya/leetCode
:pencil2: 算法相关知识储备 LeetCode with Python :books:
shaoxiongji/fed-att
Attentive Federated Learning for Private NLM
AshwinRJ/Federated-Learning-PyTorch
Implementation of Communication-Efficient Learning of Deep Networks from Decentralized Data
ebagdasa/backdoor_federated_learning
Source code for paper "How to Backdoor Federated Learning" (https://arxiv.org/abs/1807.00459)
shaoxiongji/federated-learning
A PyTorch Implementation of Federated Learning http://doi.org/10.5281/zenodo.4321561
roxanneluo/Federated-Learning
SAP-samples/machine-learning-diff-private-federated-learning
Simulate a federated setting and run differentially private federated learning.
datawhalechina/pumpkin-book
《机器学习》(西瓜书)公式详解
yunjey/pytorch-tutorial
PyTorch Tutorial for Deep Learning Researchers
VVictorQi/APUE
一个学习Apue的仓库,对于每一章节的学习代码会上传下来,包含一些遇到的问题,以及解决的 一些问题,也包括了一路走来,的成长点滴,在后期后加上我的 博客地址,来记录学习的点滴、
creaink/ucom
A simple Serial-Port/TCP/UDP debugging tool.(简洁高效的串口、TCP/UDP网络调试助手)
jvaelen/2048
A C++/Qt clone of the popular 2048 game
996icu/996.ICU
Repo for counting stars and contributing. Press F to pay respect to glorious developers.
npapernot/multiple-teachers-for-privacy
This repository contains binaries for the multiple teacher approach to learning differential private ML models: https://arxiv.org/abs/1610.05755
creaink/creaink.github.io
creaink.github.io
mozilla/rappor-aggregator
One-off notebook for performing RAPPOR aggregations
mzhilyaev/testrappor
testing rappor performance
evilying/rappor
ppmlguy/DP-AGD
Concentrated Differentially Private Gradient Descent with Adaptive per-iteration Privacy Budget