hello-agents
📚 《从零开始构建智能体》——从零开始的智能体原理与实践教程
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📚 《从零开始构建智能体》——从零开始的智能体原理与实践教程
GitHub describes it as 📚 从零开始构建大模型. The repository metadata lists Jupyter Notebook as its primary language. The metadata lists the NOASSERTION license. This article stays within the project description and details documented in the GitHub repository README.
Project brief: Linux /Lora LLM / MLLM. "Open Source Large Model Eating Guide" is a tutorial for rapid fine-tuning (full parameters/Lora) and deployment of domestic and foreign open source large models (LLM)/multimodal large models (MLLM) based on Linux environment tailored for Chinese babies.
💻 vibe coding 101|The first course for AI-native product builders.
本项目是一个面向小白开发者的大模型应用开发教程,在线阅读地址:https://datawhalechina.github.io/llm-universe/
🔍大模型应用开发实战一:RAG 技术全栈指南,在线阅读地址:https://datawhalechina.github.io/all-in-rag/
推荐系统入门教程,在线阅读地址:https://datawhalechina.github.io/fun-rec/
AI for All: The First Systematic Vibe Coding Tutorial | From Zero to Full-Stack, Bring Your Ideas to Life | Live at: www.vibevibe.cn ;全民AI学习第一课,首个系统化 Vibe Coding 开源教程 | 零基础到全栈实战,让人人都能借助 AI 实现自己的想法与创意 | 在线地址:www.vibevibe.cn
数据挖掘、计算机视觉、自然语言处理、推荐系统竞赛知识、代码、思路
仅需Python基础,从0构建大语言模型;从0逐步构建GLM4\Llama3\RWKV6, 深入理解大模型原理
Datawhale成员整理的面经,内容包括机器学习,CV,NLP,推荐,开发等,欢迎大家star
HuggingLLM, Hugging Future.
📚 《Deep Agents 实战》—— LangChain 官方大使出品,基于 LangChain / LangGraph 生态,从零构建生产级 AI Agent 的完整指南
哈喽!龙虾 🙋♀️ Adopt from scratch and build your first claw 🦞 来领养你的第一只龙虾!
机器学习方法习题解答,在线阅读地址:https://datawhalechina.github.io/statistical-learning-method-solutions-manual
《机器学习理论导引》(宝箱书)的证明、案例、概念补充与参考文献讲解。
Covers pre-training data, Tokenizer, Transformer, MoE,distributed training, Scaling Laws, inference & alignment .6 progressive code assignments for full-stack LLM learning | 涵盖预训练数据、分词器、Transformer、MoE、分布式训练、缩放定律、推理与对齐,6 项渐进代码作业,掌握 LLM 全栈知识
A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend.
从 NLP 到 LLM 的算法全栈教程,在线阅读地址:https://datawhalechina.github.io/base-llm/
Official SGLang × Datawhale course on LLM inference (中英双语): understand inference, build a mini-sglang from scratch, then read the real SGLang source and land your first PR. 《从零手搓SGLang》:读懂推理,手搓 mini-sglang,吃透 SGLang 源码。