Popular GitHub Repositories for Experimental Design

? Lobe Chat - an open-source, modern-design AI chat framework. Supports Multi AI Providers( OpenAI / Claude 3 / Gemini / Ollama / DeepSeek / Qwen), Knowledge Base (file upload / knowledge management / RAG ), Multi-Modals (Plugins/Artifacts) and Thinking. One-click FREE deployment of your private ChatGPT/ Claude / DeepSeek application.

?「AI 低代码平台」前后端分离 SpringBoot 2.x/3.x，SpringCloud，Ant Design&Vue3，Mybatis，Shiro！强大的代码生成器让前后端代码一键生成，无需写任何代码! 引领AI低代码开发模式 AI生成->OnlineCoding->代码生成->手工MERGE，帮助Java项目解决80%重复工作，让开发更关注业务，提高开发效率、节省成本，同时又不失灵活性

Learn how to design, develop, deploy and iterate on production-grade ML applications.

? Web File Browser

A neural network that transforms a design mock-up into a static website.

Creative Coding: Generative Art, Data visualization, Interaction Design, Resources.

A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"

The Generative AI Landscape - A Collection of Awesome Generative AI Applications

?? ShortGPT - Experimental AI framework for youtube shorts / tiktok channel automation

This repo is meant to serve as a guide for Machine Learning/AI technical interviews.

Venom is a high-performance system developed with JavaScript to create a bot for WhatsApp, support for creating any interaction, such as customer service, media sending, sentence recognition based on artificial intelligence and all types of design architecture for WhatsApp.

(CGCSTCD'2017) An easy, flexible, and accurate plate recognition project for Chinese licenses in unconstrained situations. CGCSTCD = China Graduate Contest on Smart-city Technology and Creative Design

A concise but complete full-attention transformer with a set of promising experimental features from various papers

Tools to Design or Visualize Architecture of Neural Network

A guideline for building practical production-level deep learning systems to be deployed in real world applications.

《Machine Learning Systems: Design and Implementation》- Chinese Version

Facilitating the design, comparison and sharing of deep text matching models.

The LLM's practical guide: From the fundamentals to deploying advanced LLM and RAG apps to AWS using LLMOps best practices

A collection of scientific methods, processes, algorithms, and systems to build stories & models.

A collection of scientific methods, processes, algorithms, and systems to build stories & models.