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Secret
- Yantai, China
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14:25
(UTC +08:00) - https://blog.csdn.net/qq_21579045
Stars
💨 Fast, Async-ready, Openapi, type hints based framework for building APIs
Toolkit for linearizing PDFs for LLM datasets/training
Dify is an open-source LLM app development platform. Dify's intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting yo…
[NeurIPS 2024] Official code for PuLID: Pure and Lightning ID Customization via Contrastive Alignment
A generative world for general-purpose robotics & embodied AI learning.
A simple zero-config tool to make locally trusted development certificates with any names you'd like.
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…
A Comprehensive Toolkit for High-Quality PDF Content Extraction
Collection of publicly available IPTV channels from all over the world
✯ 可直连访问的电视/广播图标库与相关工具项目 ✯ 🔕 永久免费 直连访问 完整开源 不断完善的台标 支持IPv4/IPv6双栈访问 🔕
Vue数据可视化组件库(类似阿里DataV,大屏数据展示),提供SVG的边框及装饰、图表、水位图、飞线图等组件,简单易用,长期更新(React版已发布)
So your teacher asked you to upload written assignments? Hate writing assigments? This tool will help you convert your text to handwriting xD
Python - 100天从新手到大师
Atom package - Activate POWER MODE to write your code in style.
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
强国通 科技强国 学习强国 xuexiqiangguo 全网最好用开源网页学习强国助手:TechXueXi (懒人刷分工具 自动学习)技术强国,支持答题,支持 docker 45分/天
「Java学习+面试指南」一份涵盖大部分 Java 程序员所需要掌握的核心知识。准备 Java 面试,首选 JavaGuide!
Kite Autocomplete Extension for JupyterLab
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
把萌萌哒的看板娘抱回家 (ノ≧∇≦)ノ | Live2D widget for web platform
[Unofficial] qBittorrent Enhanced, based on qBittorrent
Example source code accompanying O'Reilly's "Hadoop: The Definitive Guide" by Tom White
Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media