Stars
A feature-rich command-line audio/video downloader
Effortless data labeling with AI support from Segment Anything and other awesome models.
The Go-To Choice for CV Data Visualization, Annotation, and Model Analysis.
This package contains the original 2012 AlexNet code.
3D BIM IFC Viewer SDK for AEC engineering applications. Open Source JavaScript Toolkit based on pure WebGL for top performance, real-world coordinates and full double precision
Incredibly fast JavaScript runtime, bundler, test runner, and package manager – all in one
Toolkit for linearizing PDFs for LLM datasets/training
PDF scientific paper translation with preserved formats - 基于 AI 完整保留排版的 PDF 文档全文双语翻译，支持 Google/DeepL/Ollama/OpenAI 等服务，提供 CLI/GUI/MCP/Docker/Zotero
🤖 Chat with your SQL database 📊. Accurate Text-to-SQL Generation via LLMs using RAG 🔄.
Visualizer for neural network, deep learning and machine learning models
Super-Sql 是一个基于国内外先进生成式大模型的Java框架，专注于将数据库表结构通过检索增强生成（RAG, Retrieval-Augmented Generation）技术进行训练，从而实现从自然语言文本到SQL查询的智能转换（Text to SQL）。该框架旨在简化复杂的数据库查询过程，使开发者和用户能够通过简单的自然语言描述获取所需数据。
OpenOCR: A general OCR system with accuracy and efficiency. Supporting 24 Scene Text Recognition methods trained from scratch on large-scale real datasets, and will continue to add the latest methods.
Get up and running with Llama 3.3, DeepSeek-R1, Phi-4, Gemma 3, Mistral Small 3.1 and other large language models.
User-friendly AI Interface (Supports Ollama, OpenAI API, ...)
Codes of my MOOC Course <Play with Linear Algebra>. Updated contents and practices are also included. 我在慕课网上的课程《玩转线性代数》示例代码，使用Python语言。课程的更多更新内容及辅助练习也将逐步添加进这个代码仓。
[NeurIPS 2023 Spotlight] LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios (awesome MCTS)
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
A curated list of awesome Deep Learning tutorials, projects and communities.
《李宏毅深度学习教程》（李宏毅老师推荐👍，苹果书🍎），PDF下载地址：https://github.com/datawhalechina/leedl-tutorial/releases
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scutjy2015@163.com 版权所有，违权必究 Tan 2018.06
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…
Transformer Explained Visually: Learn How LLM Transformer Models Work with Interactive Visualization