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Naive Ui Admin 是一款基于 Vue3、Vite3 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
Lightning ⚡️ fast forecasting with statistical and econometric models.
Hysteria2, TUIC, Reality, ShadowTLS, WebSocket, gRPC, Naive and Warp installer script + client side config examples
NaiveBayes classifier for JavaScript
🥄✨Time-series Benchmark methods that are Simple and Probabilistic
A Python implementation of Naive Bayes from scratch.
This repository contains CO1 reference sets that can be used with the RDP Classifier, BLAST, or SINTAX to classify COI metabarcode sequences.
🏄♂️ 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 🎊
:eyes: Tobii Eye Tracker 4C Naïve Solution
Several common methods of matrix multiplication are implemented on CPU and Nvidia GPU using C++11 and CUDA.
Cross-platform binaries for cronet (Chromium Network Stack)
cool-admin for Naive UI
🗂️ 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)和标签页命名空间缓存管理. 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
A naive algorithm to identify corners on a image
I implemented a Naive Bayes classifier form scratch and applied it on MNIST dataset.
Analysis-of-NSL-KDD-dataset-using-NaiveBayes--DecisionTree--RandomForest
😎 自己写的一些 Awesome for Vite 5 / Vue3 / Webpack 5 / Element Plus / NaiveUI Apps written by myself 💯
I've built this chatbot from scratch. To build this, I've chosen three types of classification models: Naive Bayes, SVM and Logistic Regression.
Using Machine Learning to heart disease prediction with Python
Linear Algebra library in GDScript for Godot Engine. Also available as C++ GDNative plugin!
naiveui-celeris-admin 抽取web样式CSS 基于 vite, vue3, naiveui
This is a repository for NLP course projects by deeplearning.ai on Coursera.
Created various Information Retrival Algorithms from scratch in python
Solving Travelling Salesman / Salesperson ( TSP ) - using different algorithms such as Naive ( Brute Force ), Greedy and Integer Programming using Pulp
Predictive analysis of the classical 'Sales Win/Loss' dataset
simple desktop app that apply some of DNA searching algorithms, with easy use desktop GUI using Pyqt6 lib in python.
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.
Predicting what type of sentiments will be expressed depending on the type of tweet written and the location of the account. Find the best model to best predict the sentiments expressed over.Social media has become a huge part of our life. It connects people to the outer world. Social media provides a way to showcase our lives, discretely, conveniently, and on our own terms. People rely more on the posts and tweets shared on social networking sites like Twitter®, Facebook®, and Instagram®. It is anticipated that social media should guide people in getting correct and authentic information on Corona cases. There are various classification models used in machine learning. Depending on the features, accuracy, and MSE, a good model should be chosen, so it is easier to predict the sentiments that will be expressed before the tweet is written and posted