📚 Freely available programming books

分享 GitHub 上有趣、入门级的开源项目。Share interesting, entry-level open source projects on GitHub.

《动手学深度学习》：面向中文读者、能运行、可讨论。中英文版被70多个国家的500多所大学用于教学。

🌐 Make websites accessible for AI agents. Automate tasks online with ease.

Drop in a screenshot and convert it to clean code (HTML/Tailwind/React/Vue)

Turn any PDF or image document into structured data for your AI. A powerful, lightweight OCR toolkit that bridges the gap between images/PDFs and LLMs. Supports 100+ languages.

Transforms complex documents like PDFs into LLM-ready markdown/JSON for your Agentic workflows.

AiLearning：数据分析+机器学习实战+线性代数+PyTorch+NLTK+TF2

ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型

OCR software, free and offline. 开源、免费的离线OCR软件。支持截屏/批量导入图片，PDF文档识别，排除水印/页眉页脚，扫描/生成二维码。内置多国语言库。

2025年最新总结，阿里，腾讯，百度，美团，头条等技术面试题目，以及答案，专家出题人分析汇总。

⚡ A Fast, Extensible Progress Bar for Python and CLI

Deezer source separation library including pretrained models.

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

SearXNG is a free internet metasearch engine which aggregates results from various search services and databases. Users are neither tracked nor profiled.

The Inter font family

Python package built to ease deep learning on graph, on top of existing DL frameworks.

[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection"

all kinds of text classification models and more with deep learning

Open Source Neural Machine Translation and (Large) Language Models in PyTorch

a state-of-the-art-level open visual language model | 多模态预训练模型

Multilingual Document Layout Parsing in a Single Vision-Language Model

Data processing for and with foundation models! 🍎 🍋 🌽 ➡️ ➡️🍸 🍹 🍷

[NeurIPS 2023] Official implementation of the paper "Segment Everything Everywhere All at Once"

CNN-RNN中文文本分类，基于TensorFlow

Code samples from the "Python Cookbook, 3rd Edition", published by O'Reilly & Associates, May, 2013.

Implementation and experiments of graph embedding algorithms.