Hysteria2, TUIC, Reality, ShadowTLS, WebSocket, gRPC, Naive and Warp installer script + client side config examples

🏄‍♂️ 一个简洁的 NaiveUI + Vite 5 + Vue3 + TSX + Pinia + UnoCSS + Unplugin + ESLint(v9) + Vitest 的 B 端后台 Admin 原型模板框架, 开箱即用, 内置模块化管理、Commit 规范检测 Husky + lint-staged 、路由鉴权、暗黑模式、Unplugin Auto 自动导入, 适合快速搭建和二次开发实际业务场景, 持续更新最新技术栈 🎊

🔥An artistic "twitter like" community built on gin+zinc+vue+ts 清新文艺微社区

Lightning ⚡️ fast forecasting with statistical and econometric models.

搭建 SS/SSR/V2Ray/Trojan/Socks5 透明代理的 Shell 脚本

Naive Ui Admin 是一款基于 Vue3、Vite2 和 TypeScript 的先进中后台解决方案，集成了前沿的前端技术栈和典型业务模型。它拥有二次封装组件、动态菜单、权限校验、粒子化权限控制等核心功能，旨在帮助企业快速构建高质量的中后台项目。无论在新技术运用或业务实践层面，都能为您提供有力支持，并将持续更新，以满足您不断变化的需求

🗂️ 一个 Vue3 的多 Tabs 标签页切换选项卡的演示模板, 🔨支持路由表自动生成多页签、页面缓存(KeepAlive)和标签页缓存管理. 基于 Vite 5 + Pinia 2 + TypeScript + Naive UI + Vue Router 4 + UnoCSS + Unplugin + ESLint(v9) + Vitest, 开箱即用的解决方案, 快速开发中后台前端，可用于学习和参考

Multi-protocol script mainly for setting up udp based protocols

Signal Processing & Machine Learning

Airline passenger traffic prediction using time series forecasting techniques

😎 自己写的一些 Awesome for Vite 5 / Vue 3 / Webpack 5 Apps written by myself 💯

Cross-platform binaries for cronet (Chromium Network Stack)

simple desktop app that apply some of DNA searching algorithms, with easy use desktop GUI using Pyqt6 lib in python.

Compare directories by fuzzy-matching file attributes without checking contents

🥄✨Time-series Benchmark methods that are Simple and Probabilistic

This repository houses two Python scripts for string matching algorithms - 'naive_matching' and 'booyer-moore-horspool.' These scripts are designed to efficiently search for occurrences of one string (the pattern) within another (the text).

cool-admin for Naive UI

I've built this chatbot from scratch. To build this, I've chosen three types of classification models: Naive Bayes, SVM and Logistic Regression.

This repository contains CO1 reference sets that can be used with the RDP Classifier, BLAST, or SINTAX to classify COI metabarcode sequences.

Predictive problems requires three main challenges to overcome. First, choosing the right classification algorithm. Second, building a robust building and testing environment for algorithm to learn and thirdly, picking the appropriate performance metric for evaluation. Here it is explained how these challenges can be addressed.