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My continuously updated Machine Learning, Probabilistic Models and Deep Learning notes and demos (2000+ slides) 我不间断更新的机器学习，概率模型和深度学习的讲义(2000+页)和视频链接
从 MiniMind 源码读起,再延伸到现代大模型技术体系的中文学习笔记。主线逐行精读预训练 / SFT / DPO / PPO / GRPO 与训练机制;附录 17 篇进阶卷覆盖量化、投机解码、RLHF 全景、模型代际史等 MiniMind 没涉及、但进阶绕不开的主题。
AI Infrastructure Engineer Learning Track - Production ML infrastructure curriculum (2-4 years experience)
Opinionated skills for AI coding agents to create stunning diagrams and visualizations directly in Markdown. These skills extend agent capabilities across diagram generation, data visualization, an…
Agent Reinforcement Trainer: train multi-step agents for real-world tasks using GRPO. Give your agents on-the-job training. Reinforcement learning for Qwen3.6, GPT-OSS, Llama, and more!
AI agents running research on single-GPU nanochat training automatically
Local UI to run and train LLMs and diffusion models, including Qwen3.8, Kimi K3, MiniMax-H3, Gemma 4, DeepSeek-V4, FLUX and more.
AutoClip : AI-powered video clipping and highlight generation · 一款智能高光提取与剪辑的二创工具
🧠「大模型」2小时完全从0训练64M的小参数LLM！Train a 64M-parameter LLM from scratch in just 2h!
👀「大模型」2小时从0训练65M参数的视觉多模态VLM！Train a 65M-parameter VLM from scratch in just 2h!
分享AI Infra知识&代码练习：PyTorch、vLLM/SGLang、slime/vime框架入门⚡️、性能加速🚀、大模型基础🧠、AI软硬件🔧等
"Paper2Slides: From Paper to Presentation in One Click"
A curated list of 100+ libraries and frameworks for AI engineers building with LLMs
Qwen3-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.
This repo is meant to serve as a guide for Machine Learning/AI technical interviews.
Data Structure Algorithms, (GenAI/ML) System Design, Machine Learning, DevOps coding interview practices
⚡FlashRAG: A Python Toolkit for Efficient RAG Research (WWW2025 Resource)
💫 Toolkit to help you get started with Spec-Driven Development
《动手学大模型Dive into LLMs》系列编程实践教程
✅（已完结）超级全面的 深度学习 笔记【土堆 Pytorch】【李沐 动手学深度学习】【吴恩达 深度学习】【大飞 大模型Agent】
AI审计智能体应用平台 - AgenticAudit 将重复性工作交给AI，让专业人士回归本质价值 AgenticAudit 致力于成为审计专家的智能 Copilot
End-to-end, code-first tutorials for building production-grade GenAI agents. From prototype to enterprise deployment.