[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-curiousily--AI-Bootcamp":3,"tool-curiousily--AI-Bootcamp":62},[4,18,28,37,45,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":24,"last_commit_at":25,"category_tags":26,"status":17},9989,"n8n","n8n-io\u002Fn8n","n8n 是一款面向技术团队的公平代码（fair-code）工作流自动化平台，旨在让用户在享受低代码快速构建便利的同时，保留编写自定义代码的灵活性。它主要解决了传统自动化工具要么过于封闭难以扩展、要么完全依赖手写代码效率低下的痛点，帮助用户轻松连接 400 多种应用与服务，实现复杂业务流程的自动化。\n\nn8n 特别适合开发者、工程师以及具备一定技术背景的业务人员使用。其核心亮点在于“按需编码”：既可以通过直观的可视化界面拖拽节点搭建流程，也能随时插入 JavaScript 或 Python 代码、调用 npm 包来处理复杂逻辑。此外，n8n 原生集成了基于 LangChain 的 AI 能力，支持用户利用自有数据和模型构建智能体工作流。在部署方面，n8n 提供极高的自由度，支持完全自托管以保障数据隐私和控制权，也提供云端服务选项。凭借活跃的社区生态和数百个现成模板，n8n 让构建强大且可控的自动化系统变得简单高效。",184740,2,"2026-04-19T23:22:26",[16,14,13,15,27],"插件",{"id":29,"name":30,"github_repo":31,"description_zh":32,"stars":33,"difficulty_score":10,"last_commit_at":34,"category_tags":35,"status":17},10095,"AutoGPT","Significant-Gravitas\u002FAutoGPT","AutoGPT 是一个旨在让每个人都能轻松使用和构建 AI 的强大平台，核心功能是帮助用户创建、部署和管理能够自动执行复杂任务的连续型 AI 智能体。它解决了传统 AI 应用中需要频繁人工干预、难以自动化长流程工作的痛点，让用户只需设定目标，AI 即可自主规划步骤、调用工具并持续运行直至完成任务。\n\n无论是开发者、研究人员，还是希望提升工作效率的普通用户，都能从 AutoGPT 中受益。开发者可利用其低代码界面快速定制专属智能体；研究人员能基于开源架构探索多智能体协作机制；而非技术背景用户也可直接选用预置的智能体模板，立即投入实际工作场景。\n\nAutoGPT 的技术亮点在于其模块化“积木式”工作流设计——用户通过连接功能块即可构建复杂逻辑，每个块负责单一动作，灵活且易于调试。同时，平台支持本地自托管与云端部署两种模式，兼顾数据隐私与使用便捷性。配合完善的文档和一键安装脚本，即使是初次接触的用户也能在几分钟内启动自己的第一个 AI 智能体。AutoGPT 正致力于降低 AI 应用门槛，让人人都能成为 AI 的创造者与受益者。",183572,"2026-04-20T04:47:55",[13,36,27,14,15],"语言模型",{"id":38,"name":39,"github_repo":40,"description_zh":41,"stars":42,"difficulty_score":10,"last_commit_at":43,"category_tags":44,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":46,"name":47,"github_repo":48,"description_zh":49,"stars":50,"difficulty_score":24,"last_commit_at":51,"category_tags":52,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",161147,"2026-04-19T23:31:47",[14,13,36],{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":59,"last_commit_at":60,"category_tags":61,"status":17},8272,"opencode","anomalyco\u002Fopencode","OpenCode 是一款开源的 AI 编程助手（Coding Agent），旨在像一位智能搭档一样融入您的开发流程。它不仅仅是一个代码补全插件，而是一个能够理解项目上下文、自主规划任务并执行复杂编码操作的智能体。无论是生成全新功能、重构现有代码，还是排查难以定位的 Bug，OpenCode 都能通过自然语言交互高效完成，显著减少开发者在重复性劳动和上下文切换上的时间消耗。\n\n这款工具专为软件开发者、工程师及技术研究人员设计，特别适合希望利用大模型能力来提升编码效率、加速原型开发或处理遗留代码维护的专业人群。其核心亮点在于完全开源的架构，这意味着用户可以审查代码逻辑、自定义行为策略，甚至私有化部署以保障数据安全，彻底打破了传统闭源 AI 助手的“黑盒”限制。\n\n在技术体验上，OpenCode 提供了灵活的终端界面（Terminal UI）和正在测试中的桌面应用程序，支持 macOS、Windows 及 Linux 全平台。它兼容多种包管理工具，安装便捷，并能无缝集成到现有的开发环境中。无论您是追求极致控制权的资深极客，还是渴望提升产出的独立开发者，OpenCode 都提供了一个透明、可信",144296,1,"2026-04-16T14:50:03",[13,27],{"id":63,"github_repo":64,"name":65,"description_en":66,"description_zh":67,"ai_summary_zh":67,"readme_en":68,"readme_zh":69,"quickstart_zh":70,"use_case_zh":71,"hero_image_url":72,"owner_login":73,"owner_name":74,"owner_avatar_url":75,"owner_bio":76,"owner_company":76,"owner_location":77,"owner_email":78,"owner_twitter":79,"owner_website":80,"owner_url":81,"languages":82,"stars":87,"forks":88,"last_commit_at":89,"license":90,"difficulty_score":59,"env_os":91,"env_gpu":92,"env_ram":93,"env_deps":94,"category_tags":108,"github_topics":109,"view_count":24,"oss_zip_url":76,"oss_zip_packed_at":76,"status":17,"created_at":121,"updated_at":122,"faqs":123,"releases":124},10044,"curiousily\u002FAI-Bootcamp","AI-Bootcamp","Self-paced bootcamp on Generative AI. Tutorials on ML fundamentals, Ollama, LLMs, RAGs, LangChain, LangGraph, Fine-tuning, DSPy & AI Agents (CrewAI), (Using ChatGPT, gpt-oss, Claude, Qwen, Gemma, Llama, Gemini)","AI-Bootcamp 是一个专注于生成式 AI 的自助式实战训练营，旨在帮助学习者从零开始掌握构建真实世界 AI 应用的核心技能。它解决了传统教程中理论与工程实践脱节的问题，不仅涵盖机器学习基础、数学原理和 Python essentials，更深入讲解了大语言模型（LLM）、检索增强生成（RAG）、LangChain、LangGraph、模型微调以及智能体（如 CrewAI）等前沿技术的落地应用。\n\n该项目特别适合希望转型或进阶的 AI 工程师、开发者以及具备一定编程基础的技术研究人员。与普通科普不同，AI-Bootcamp 强调“动手完成”，课程内容包括从数据探索、PyTorch 深度学习框架实战，到 MLOps 生产系统部署的全流程指导。其独特亮点在于提供了基于 Ollama、Llama、Qwen、Gemma 等多种开源模型的实操案例，并配套了 Colab 笔记本、视频教程及活跃的 Discord 社区支持，让用户能在真实代码环境中快速上手，真正具备将 AI 模型从概念转化为生产级服务的能力。","# AI Bootcamp\n\n[![Open In Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fcuriousily\u002FAI-Bootcamp\u002F)\n[![](https:\u002F\u002Fdcbadge.vercel.app\u002Fapi\u002Fserver\u002FUaNPxVD6tv?style=flat)](https:\u002F\u002Fdiscord.gg\u002FUaNPxVD6tv)\n[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fchannel\u002Fsubscribers\u002FUCoW_WzQNJVAjxo4osNAxd_g?label=Watch%20on%20YouTube)](https:\u002F\u002Fbit.ly\u002Fvenelin-subscribe)\n[![](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fcuriousily\u002FAI-Bootcamp)](https:\u002F\u002Fgithub.com\u002Fcuriousily\u002FAI-Bootcamp\u002Fblob\u002Fmaster\u002FLICENSE)\n\nThe \"Get Shit Done with AI\" Bootcamp focuses on real-world applications that will equip you with the skills and knowledge to become a great AI engineer.\n\n- Join the [Discord community](https:\u002F\u002Fdiscord.com\u002Finvite\u002FUaNPxVD6tv)\n- Watch the [YouTube channel](https:\u002F\u002Fbit.ly\u002Fvenelin-subscribe)\n- Join the [AI Engineering Academy](https:\u002F\u002Fwww.mlexpert.io\u002F)\n\n## AI\u002FML Foundations\n\nMaster the core code and concepts, from Python essentials to your first powerful machine learning model\n\n| Lesson | Description | Tutorial | Video |\n| --------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------- | ----- |\n| Python Essentials for AI Engineering | Master Python data structures, functional tricks, typing, JSON, pathlib, NumPy, and Pandas - all distilled for machine-learning engineers. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Ffoundations\u002Fpython-essentials) | |\n| Mathematics is the Language of AI | Build rock-solid intuition for AI: grasp the essentials of linear algebra, calculus, and probability through hands-on Python examples and practical engineering tips | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Ffoundations\u002Fmathematics-for-ai) | |\n| Start Simple - The Power of Linear Models | Learn how and why to build strong, interpretable baselines: explore linear regression end-to-end, from feature scaling to evaluation, with hands-on notebooks and real data. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Ffoundations\u002Flinear-models) | |\n| Essential PyTorch for Real-World Applications | Hands-on PyTorch fundamentals: tensors, autograd, data loading, optimizers, and full training loops - everything you need to build and deploy deep-learning models in production. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Ffoundations\u002Freal-world-pytorch) | |\n\n## MLOps and Production Systems\n\nDon't just build models - ship them. Master the production lifecycle from data pipelines to live API deployment.\n\n| Lesson | Description | Tutorial | Video |\n| ---------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------ | ----- |\n| Understanding Your Data - Data Exploration | Master data exploration for AI production. Analyze the Bank Marketing dataset using Pandas\u002FSeaborn to understand distributions, find issues (missing data, outliers), and inform reliable data validation & preprocessing pipelines. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fml-in-production\u002Fdata-exploration) | |\n| Fueling Production AI - Data Validation & Pipelines | Master robust data pipelines: validate raw data with pandera, engineer features with scikit-learn Pipelines, and version everything with DVC for reliable ML in production. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fml-in-production\u002Fdata-validation-and-processing) | |\n| Reproducible Training - ML Pipelines & Experiment Tracking | Discover reproducible ML training: build DVC-driven pipelines, track experiments with MLflow, and tune LightGBM models for real-world impact in this hands-on tutorial. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fml-in-production\u002Fmachine-learning-pipelines) | |\n| From Model to Service - Building and Dockerizing APIs | Take your trained machine learning model and build a production-ready REST API using FastAPI. Then, learn to package your application and all its dependencies into a portable Docker container. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fml-in-production\u002Fmodel-to-container) | |\n| Serving at Scale - Cloud Deployment with AWS | Learn to deploy a containerized ML model to the cloud. This guide covers pushing artifacts to S3, storing your Docker image in ECR, and orchestrating deployment with AWS ECS and EC2. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fml-in-production\u002Fcloud-deployment) | |\n\n## AI Systems Engineering\n\nMaster the full-stack toolkit for building cutting-edge applications on top of Large Language Models.\n\n| Lesson | Description | Tutorial | Video |\n| --------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------- | ---------------------------------------------------- |\n| Run AI Models Locally - Ollama Quickstart | Get started with local AI development. Learn to install and use Ollama to run powerful AI models on your own machine for enhanced privacy, speed, and cost-efficiency. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-systems-engineering\u002Flocal-ai-quickstart) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=lmFCVCqOlz8) |\n| Prompt Engineering | Learn how to write effective prompts for AI models using a battle-tested template | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-systems-engineering\u002Fprompt-engineering) | |\n| The AI Engineer Toolkit - APIs, structured output, tools | Learn to use APIs, structured output, and tools to enhance your LLMs applications | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-systems-engineering\u002Fai-engineer-toolkit) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=10Pixhd9f9k) |\n| LangChain Foundations - An Engineer's Guide | Master the essentials of LangChain, the go-to framework for building robust LLM applications. Learn to manage prompts, enforce structured outputs with Pydantic, and build a simple RAG pipeline to chat with your documents. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-systems-engineering\u002Flangchain-foundations) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=W8XKeV94xhk) |\n| Connect AI to External Systems - Model Context Protocol | Learn to connect AI\u002FLLMs to external systems using the Model Context Protocol (MCP). This hands-on tutorial guides AI engineers through building MCP servers and clients with Python, Ollama, and Streamlit, solving complex integration challenges with a standardized approach. Build a practical todo list agent. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-systems-engineering\u002Fmodel-context-protocol) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=aiH79Q-LGjY) |\n| Build Your Own Dataset with Knowledge Distillation | Use a powerful LLM as a 'teacher' to automatically label raw data and create custom datasets for training and evaluating specialized models. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-systems-engineering\u002Fbuild-your-own-dataset) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ryUovb6qZjw) |\n| No More Manual Tweaking - Automated Prompt Engineering | Learn to use DSPy to automatically optimize your prompts, turning a mediocre baseline into a high-performing pipeline. Use a powerful 'prompt model' to teach a smaller, faster 'task model' how to excel at financial sentiment analysis. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-systems-engineering\u002Fautomated-prompt-engineering) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=VN5yseWStX4) |\n| Lies, Damn Lies and Hallucinations - Evaluating your LLMs | How do you know if your LLM is good? Evaluating your LLMs is a crucial step in building reliable AI applications that provide useful and accurate results. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-systems-engineering\u002Fllm-evaluation) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=sD6QOvKm9eY) |\n| Train Your Model - Fine-Tuning LLM | Learn how to fine-tune an open-source LLM into a specialized expert for your specific task. Master the complete engineering workflow from data prep and QLoRA training to evaluation and deployment on the Hugging Face Hub. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-systems-engineering\u002Ffine-tuning-llm) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=jftzWenANnw) |\n\n## RAG and Context Engineering\n\nConnect LLMs to external and unstructured data sources, so they can answer with up-to-date and private knowledge.\n\n| Lesson | Description | Tutorial | Video |\n| ------------------------------------------------------------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- | ---------------------------------------------------- |\n| Build a Chatbot with Memory | Learn how to build a chatbot that acts as a wellness coach using LangChain and Streamlit | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fcontext-engineering\u002Fbuild-chatbot) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=XdbIv7AE3VA) |\n| Use External Knowledge - Build a Cache-Augmented Generation (CAG) System | Learn to build a local Cache-Augmented Generation (CAG) system using LangChain and Ollama. Process documents and leverage full LLM context for knowledge tasks without retrieval. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fcontext-engineering\u002Fcache-augmented-generation) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=r6-3y7g8bw4) |\n| Create Knowledge for Your Models - Document Processing | Learn how to convert documents into knowledge for your AI applications. Process PDF files, including their images and tables, into structured data. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fcontext-engineering\u002Fdocument-processing-for-ai) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=B5XD-qpL0FU) |\n| Break It Down Right - Effective Chunking Strategies | Master the most critical step in RAG - chunking. Learn to move beyond simple splitting with structure-aware, semantic, and LLM-driven chunking techniques to build a knowledge base that powers context-aware AI. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fcontext-engineering\u002Feffective-chunking-strategies) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Lk6D1huUK0s) |\n| Search by Meaning - Embeddings and Vector Databases | Transform your text chunks into a searchable knowledge base. Learn to create semantic embeddings, perform similarity searches, and store your vectors in a production-ready database like Supabase with pgvector. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fcontext-engineering\u002Fembeddings-and-vector-databases) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=EwNzlrZBrA0) |\n| Beyond Vector Search - Retrieving the Right Context | Upgrade your prototype RAG into a dependable, production-grade retriever. Combine BM25 and vector search, add a fast re-ranker, and use query reformulation (HyDE) to deliver precise, citable context to your LLM, keeping answers accurate and trustworthy. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fcontext-engineering\u002Fadvanced-retrieval) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=YNcoFoRwoc8) |\n| Build a Retrieval-Augmented Generation System | Learn to build an advanced Retrieval-Augmented Generation (RAG) system using LangChain, Ollama, and hybrid search. Process documents, create embeddings, and query your knowledge base with a local LLM. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fcontext-engineering\u002Frag-pipelines) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Fyry6WO9nlc) |\n\n## Agents and Workflows\n\nBuild the future of automation. Design intelligent agents that can reason, plan, and execute complex tasks on their own.\n\n| Lesson | Description | Tutorial | Video |\n| ------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------- | ---------------------------------------------------- |\n| Getting Started with LangGraph - Workflows and AI Agents | Master LangGraph by building an intelligent support ticket system. Learn the critical difference between predictable, developer-controlled workflows and flexible, LLM-driven agents. This tutorial provides the foundational skills for orchestrating complex, stateful AI applications. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-agents\u002Flanggraph-getting-started) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=mRx12jkugTE) |\n| Teamwork Makes the Dream Work - Build Agentic Workflow | Build an agentic workflow that analyzes Reddit posts and generates a report based on the analysis. All using only local models. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-agents\u002Fbuild-agentic-workflow) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=dVf1z2BDVtI) |\n| Thinking and Acting - Build an AI Agent | Build an AI agent that lets you to talk to your database. Working with a local LLM using LangChain and Ollama. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-agents\u002Fbuild-ai-agent) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ay_sYadoxgk) |\n| Chat With Your Data - A Local MCP AI Agent | Build a secure, local-first AI agent that can chat with your files. This tutorial uses the Model Context Protocol (MCP), LangGraph, and Streamlit to create a powerful personal knowledge manager. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-agents\u002Fbuild-mcp-agent) | [Watch](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ZkMlWwgiFGw) |\n| Agentic RAG - Building an AI Financial Analyst Team | Build a multi-agent system with LangGraph that dynamically plans and retrieves financial data from stock APIs and SEC filings to answer complex questions, moving beyond simple RAG pipelines. | [Read](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-agents\u002Fagentic-rag) | |","# AI训练营\n\n[![在Colab中打开](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fcuriousily\u002FAI-Bootcamp\u002F)\n[![](https:\u002F\u002Fdcbadge.vercel.app\u002Fapi\u002Fserver\u002FUaNPxVD6tv?style=flat)](https:\u002F\u002Fdiscord.gg\u002FUaNPxVD6tv)\n[![](https:\u002F\u002Fimg.shields.io\u002Fyoutube\u002Fchannel\u002Fsubscribers\u002FUCoW_WzQNJVAjxo4osNAxd_g?label=在YouTube观看)](https:\u002F\u002Fbit.ly\u002Fvenelin-subscribe)\n[![](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002Fcuriousily\u002FAI-Bootcamp)](https:\u002F\u002Fgithub.com\u002Fcuriousily\u002FAI-Bootcamp\u002Fblob\u002Fmaster\u002FLICENSE)\n\n“用AI搞定一切”训练营专注于现实世界的应用，旨在帮助你掌握成为一名优秀AI工程师所需的技能和知识。\n\n- 加入[Discord社区](https:\u002F\u002Fdiscord.com\u002Finvite\u002FUaNPxVD6tv)\n- 观看[YouTube频道](https:\u002F\u002Fbit.ly\u002Fvenelin-subscribe)\n- 加入[AI工程学院](https:\u002F\u002Fwww.mlexpert.io\u002F)\n\n## AI\u002FML基础\n\n从Python essentials到你的第一个强大的机器学习模型，全面掌握核心代码与概念。\n\n| 课程 | 描述 | 教程 | 视频 |\n| --------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------- | ----- |\n| AI工程的Python essentials | 掌握Python数据结构、函数式编程技巧、类型注解、JSON、pathlib、NumPy和Pandas——所有内容都为机器学习工程师精炼提炼。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Ffoundations\u002Fpython-essentials) | |\n| 数学是AI的语言 | 建立坚实的AI直觉：通过动手实践的Python示例和实用的工程技巧，掌握线性代数、微积分和概率论的核心知识 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Ffoundations\u002Fmathematics-for-ai) | |\n| 从简单开始——线性模型的力量 | 学习如何以及为何构建强大且可解释的基准模型：通过动手笔记本和真实数据，端到端地探索线性回归，从特征缩放到模型评估。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Ffoundations\u002Flinear-models) | |\n| 面向实际应用的PyTorch基础 | PyTorch实战基础：张量、自动求导、数据加载、优化器以及完整的训练循环——构建和部署深度学习模型到生产环境所需的一切。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Ffoundations\u002Freal-world-pytorch) | |\n\n## MLOps与生产系统\n\n不要只停留在模型开发阶段——让它们真正投入使用。掌握从数据流水线到实时API部署的完整生产生命周期。\n\n| 课程 | 描述 | 教程 | 视频 |\n| ---------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------ | ----- |\n| 理解你的数据——数据探索 | 掌握面向AI生产的数据探索技术。使用Pandas\u002FSeaborn分析银行营销数据集，了解数据分布、发现潜在问题（缺失值、异常值），并为可靠的数据验证与预处理流水线提供依据。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fml-in-production\u002Fdata-exploration) | |\n| 为生产级AI提供燃料——数据验证与流水线 | 掌握稳健的数据流水线：使用pandera验证原始数据，借助scikit-learn的Pipeline进行特征工程，并通过DVC对所有内容进行版本控制，以确保生产环境中机器学习的可靠性。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fml-in-production\u002Fdata-validation-and-processing) | |\n| 可复现的训练——ML流水线与实验跟踪 | 发现可复现的机器学习训练方法：构建基于DVC的流水线，使用MLflow跟踪实验，并在此动手教程中调优LightGBM模型以实现真正的实际效果。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fml-in-production\u002Fmachine-learning-pipelines) | |\n| 从模型到服务——构建并容器化API | 将你训练好的机器学习模型利用FastAPI构建为生产就绪的REST API。随后，学习如何将应用程序及其所有依赖项打包成一个便携式的Docker容器。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fml-in-production\u002Fmodel-to-container) | |\n| 大规模服务——使用AWS进行云部署 | 学习如何将容器化的ML模型部署到云端。本指南涵盖了将制品上传至S3、将Docker镜像存储在ECR，以及使用AWS ECS和EC2编排部署的全过程。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fml-in-production\u002Fcloud-deployment) | |\n\n## 人工智能系统工程\n\n掌握构建基于大型语言模型的前沿应用所需的全栈工具集。\n\n| 课程 | 描述 | 教程 | 视频 |\n| --------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------- | ---------------------------------------------------- |\n| 在本地运行 AI 模型 - Ollama 快速入门 | 开始本地 AI 开发。学习安装和使用 Ollama，在您自己的机器上运行强大的 AI 模型，以提升隐私性、速度和成本效益。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-systems-engineering\u002Flocal-ai-quickstart) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=lmFCVCqOlz8) |\n| 提示工程 | 学习如何使用经过实战检验的模板为 AI 模型编写有效的提示语 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-systems-engineering\u002Fprompt-engineering) | |\n| 人工智能工程师工具包 - API、结构化输出、工具 | 学习如何使用 API、结构化输出和工具来增强您的 LLM 应用程序 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-systems-engineering\u002Fai-engineer-toolkit) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=10Pixhd9f9k) |\n| LangChain 基础 - 工程师指南 | 掌握 LangChain 的核心知识，它是构建稳健 LLM 应用程序的首选框架。学习管理提示、使用 Pydantic 强制实现结构化输出，并构建一个简单的 RAG 流程，以便与您的文档进行对话。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-systems-engineering\u002Flangchain-foundations) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=W8XKeV94xhk) |\n| 将 AI 连接到外部系统 - 模型上下文协议 | 学习使用模型上下文协议 (MCP) 将 AI\u002FLMM 连接到外部系统。本实践教程将指导 AI 工程师使用 Python、Ollama 和 Streamlit 构建 MCP 服务器和客户端，通过标准化方法解决复杂的集成问题。构建一个实用的待办事项代理。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-systems-engineering\u002Fmodel-context-protocol) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=aiH79Q-LGjY) |\n| 使用知识蒸馏构建您自己的数据集 | 使用强大的 LLM 作为“教师”，自动标注原始数据，创建用于训练和评估专用模型的自定义数据集。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-systems-engineering\u002Fbuild-your-own-dataset) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ryUovb6qZjw) |\n| 再也不用手动调整 - 自动化提示工程 | 学习使用 DSPy 自动优化您的提示，将平庸的基础版本转变为高性能的流程。利用强大的“提示模型”教导更小、更快的“任务模型”在金融情绪分析方面表现出色。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-systems-engineering\u002Fautomated-prompt-engineering) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=VN5yseWStX4) |\n| 谎言、该死的谎言与幻觉 - 评估您的 LLMs | 您如何判断自己的 LLM 是否优秀？评估 LLM 是构建可靠、能够提供有用且准确结果的 AI 应用程序的关键步骤。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-systems-engineering\u002Fllm-evaluation) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=sD6QOvKm9eY) |\n| 训练您的模型 - 微调 LLM | 学习如何将开源 LLM 微调为针对您特定任务的专业专家。掌握从数据准备、QLoRA 训练到评估以及在 Hugging Face Hub 上部署的完整工程流程。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-systems-engineering\u002Ffine-tuning-llm) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=jftzWenANnw) |\n\n## RAG与上下文工程\n\n将大语言模型连接到外部和非结构化数据源，使其能够基于最新且私有的知识进行回答。\n\n| 课程 | 描述 | 教程 | 视频 |\n| ------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- | ---------------------------------------------------- |\n| 构建具有记忆功能的聊天机器人 | 学习如何使用 LangChain 和 Streamlit 构建一个充当健康教练的聊天机器人 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fcontext-engineering\u002Fbuild-chatbot) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=XdbIv7AE3VA) |\n| 使用外部知识——构建缓存增强生成（CAG）系统 | 学习如何使用 LangChain 和 Ollama 构建本地缓存增强生成（CAG）系统。处理文档，并在无需检索的情况下，充分利用大语言模型的完整上下文来完成知识任务。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fcontext-engineering\u002Fcache-augmented-generation) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=r6-3y7g8bw4) |\n| 为您的模型创建知识——文档处理 | 学习如何将文档转化为适用于您的人工智能应用的知识。处理 PDF 文件，包括其中的图像和表格，将其转换为结构化数据。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fcontext-engineering\u002Fdocument-processing-for-ai) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=B5XD-qpL0FU) |\n| 正确地拆分——有效的分块策略 | 掌握 RAG 中最关键的步骤——分块。学习超越简单的按固定大小分割，采用结构感知、语义驱动以及由大语言模型引导的分块技术，构建能够支持上下文感知型 AI 的知识库。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fcontext-engineering\u002Feffective-chunking-strategies) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Lk6D1huUK0s) |\n| 按语义搜索——嵌入与向量数据库 | 将文本块转化为可搜索的知识库。学习创建语义嵌入、执行相似度搜索，并将向量存储在像 Supabase with pgvector 这样适合生产环境的数据库中。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fcontext-engineering\u002Fembeddings-and-vector-databases) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=EwNzlrZBrA0) |\n| 超越向量搜索——检索正确的上下文 | 将您的原型 RAG 升级为可靠、可用于生产的检索器。结合 BM25 和向量搜索，添加快速重排序器，并使用查询改写（HyDE）技术，为您的大语言模型提供精确且可引用的上下文，从而确保回答准确可信。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fcontext-engineering\u002Fadvanced-retrieval) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=YNcoFoRwoc8) |\n| 构建检索增强生成系统 | 学习如何使用 LangChain、Ollama 和混合搜索构建先进的检索增强生成（RAG）系统。处理文档、创建嵌入，并使用本地大语言模型查询您的知识库。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fcontext-engineering\u002Frag-pipelines) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Fyry6WO9nlc) |\n\n## 代理与工作流\n\n构建自动化未来。设计能够自主推理、规划并执行复杂任务的智能代理。\n\n| 课程 | 描述 | 教程 | 视频 |\n| ------------------------------------------------------ | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------- | ---------------------------------------------------- |\n| LangGraph 入门 - 工作流与 AI 代理 | 通过构建一个智能支持工单系统来掌握 LangGraph。了解可预测、由开发者控制的工作流与灵活、由大语言模型驱动的代理之间的关键区别。本教程将为您提供编排复杂、有状态 AI 应用程序的基础技能。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-agents\u002Flanggraph-getting-started) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=mRx12jkugTE) |\n| 团队合作成就梦想 - 构建代理式工作流 | 构建一个代理式工作流，用于分析 Reddit 帖子并根据分析结果生成报告。全程仅使用本地模型。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-agents\u002Fbuild-agentic-workflow) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=dVf1z2BDVtI) |\n| 思考与行动 - 构建 AI 代理 | 构建一个允许您与数据库对话的 AI 代理。结合 LangChain 和 Ollama 使用本地大语言模型。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-agents\u002Fbuild-ai-agent) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ay_sYadoxgk) |\n| 与您的数据聊天 - 本地 MCP AI 代理 | 构建一个安全、以本地优先的 AI 代理，可以与您的文件进行对话。本教程使用模型上下文协议 (MCP)、LangGraph 和 Streamlit，打造功能强大的个人知识管理工具。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-agents\u002Fbuild-mcp-agent) | [观看](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ZkMlWwgiFGw) |\n| 代理式 RAG - 构建 AI 金融分析师团队 | 使用 LangGraph 构建一个多代理系统，该系统能够动态规划并从股票 API 和 SEC 文件中检索金融数据，以回答复杂问题，从而超越简单的 RAG 流程。 | [阅读](https:\u002F\u002Fwww.mlexpert.io\u002Facademy\u002Fv1\u002Fai-agents\u002Fagentic-rag) | |","# AI-Bootcamp 快速上手指南\n\nAI-Bootcamp 是一个专注于实战的开源学习项目，旨在帮助开发者掌握从机器学习基础、MLOps 生产部署到大语言模型（LLM）系统构建的全栈技能。本指南将带你快速开始学习旅程。\n\n## 环境准备\n\n在开始之前，请确保你的开发环境满足以下要求：\n\n* **操作系统**: Windows, macOS 或 Linux。\n* **Python 版本**: 推荐 Python 3.9 或更高版本。\n* **包管理工具**: `pip` 或 `conda`。\n* **可选加速**: 国内用户建议配置清华源或阿里源以加速依赖下载。\n * 临时使用清华源示例：`pip install -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple \u003Cpackage_name>`\n* **云端替代方案**: 如果本地环境配置困难，可直接点击项目首页的 **[Open In Colab]** 按钮，在 Google Colab 云端环境中直接运行所有教程代码，无需本地安装。\n\n## 安装步骤\n\n本项目主要由一系列 Jupyter Notebook 教程和文档组成，没有单一的“安装包”。你可以通过克隆仓库来获取所有学习资料和代码示例。\n\n1. **克隆仓库**\n 打开终端，执行以下命令将项目下载到本地：\n ```bash\n git clone https:\u002F\u002Fgithub.com\u002Fcuriousily\u002FAI-Bootcamp.git\n cd AI-Bootcamp\n ```\n\n2. **创建虚拟环境**\n 建议使用 `venv` 或 `conda` 创建独立的虚拟环境，避免依赖冲突：\n ```bash\n python -m venv ai-bootcamp-env\n # Windows 激活\n ai-bootcamp-env\\Scripts\\activate\n # macOS\u002FLinux 激活\n source ai-bootcamp-env\u002Fbin\u002Factivate\n ```\n\n3. **安装核心依赖**\n 根据你要学习的模块（如基础 ML、MLOps 或 LLM 工程），安装相应的库。以下是涵盖大部分教程的通用依赖安装命令（国内用户请添加 `-i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple`）：\n ```bash\n pip install numpy pandas scikit-learn torch torchvision torchaudio --index-url https:\u002F\u002Fdownload.pytorch.org\u002Fwhl\u002Fcu118\n pip install jupyterlab matplotlib seaborn pandera dvc mlflow fastapi docker uvicorn\n pip install langchain langchain-community langchain-huggingface ollama streamlit dspy evaluate transformers accelerate peft\n ```\n *注：具体每个 Lesson 可能有特定的 `requirements.txt`，请以对应教程文件夹内的说明为准。*\n\n4. **启动学习界面**\n 安装完成后，启动 Jupyter Lab 开始学习：\n ```bash\n jupyter lab\n ```\n\n## 基本使用\n\nAI-Bootcamp 的使用方式是按照模块顺序阅读文档并运行对应的 Notebook 代码。以下以最基础的 **\"Python Essentials for AI Engineering\"** 为例，展示如何开始第一个练习：\n\n1. **定位教程**: 在 Jupyter Lab 中导航至 `foundations` 目录（或对应章节文件夹）。\n2. **打开 Notebook**: 找到名为 `python-essentials.ipynb` (或类似名称) 的文件并打开。\n3. **运行代码**:\n * 阅读单元格中的理论讲解。\n * 选中代码单元格，按 `Shift + Enter` 执行代码。\n \n **示例代码片段（数据操作）：**\n ```python\n import pandas as pd\n import numpy as np\n\n # 创建一个简单的 DataFrame 模拟 AI 工程中的数据预处理\n data = {\n 'feature_1': np.random.rand(5),\n 'feature_2': np.random.rand(5),\n 'target': [0, 1, 0, 1, 0]\n }\n df = pd.DataFrame(data)\n\n # 查看数据前几行\n print(df.head())\n\n # 简单的特征缩放示例\n df['feature_1_scaled'] = (df['feature_1'] - df['feature_1'].mean()) \u002F df['feature_1'].std()\n print(\"\\nScaled Feature 1:\")\n print(df['feature_1_scaled'])\n ```\n\n4. **进阶实践**:\n * **MLOps 模块**: 尝试运行 `data-validation-and-processing` 教程，体验如何使用 `pandera` 验证数据模式。\n * **LLM 模块**: 确保本地已安装 [Ollama](https:\u002F\u002Follama.com\u002F) 并拉取模型（如 `ollama pull llama3`），然后运行 `local-ai-quickstart` 教程，体验本地大模型推理。\n\n通过依次完成各个模块的 Notebook 练习，你将逐步构建起从数据处理到模型部署的完整 AI 工程能力。","某初创公司的后端工程师急需在两周内为客服系统构建一个基于私有文档的智能问答机器人，但他缺乏生成式 AI 的全栈实战经验。\n\n### 没有 AI-Bootcamp 时\n- **基础薄弱导致起步困难**：面对复杂的数学原理和 PyTorch 底层代码感到无从下手，花费大量时间补习理论却难以转化为实际代码能力。\n- **技术选型迷茫**：在 Ollama、LangChain、RAG 和各类大模型（如 Llama、Qwen）之间不知所措，无法确定适合生产环境的最佳技术组合。\n- **落地流程断裂**：仅能跑通简单的 Demo，不懂如何构建数据管道、进行模型微调或将服务部署为稳定的生产级 API。\n- **试错成本高昂**：依靠零散的网上教程摸索，频繁遇到版本兼容和环境配置问题，严重拖慢项目交付进度。\n\n### 使用 AI-Bootcamp 后\n- **快速夯实工程基石**：通过\"AI\u002FML 基础”模块，迅速掌握针对机器学习优化的 Python 技巧和 PyTorch 实战 loops，直接跳过纯理论坑洼。\n- **清晰的技术路线图**：跟随\"Generative AI\"专项教程，按部就班地学会了利用 LangGraph 编排代理、用 RAG 检索私有数据，并精准选用合适的开源模型。\n- **具备生产交付能力**：借助\"MLOps 与生产系统”章节，成功搭建了从数据探索到 API 部署的完整流水线，确保机器人能稳定上线服务。\n- **高效避坑与实战**：基于真实的案例笔记和自定步调的训练，直接在 Colab 中复现并修改代码，将原本两周的摸索期缩短为三天开发期。\n\nAI-Bootcamp 将零散的知识点串联成可落地的工程能力，帮助开发者从“只会调包”快速进阶为能独立交付生产级 AI 应用的工程师。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fcuriousily_AI-Bootcamp_30f546ce.png","curiousily","Venelin Valkov","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fcuriousily_6bb1e41a.jpg",null,"Bulgaria","venelin@curiousily.com","venelin_valkov","mlexpert.io","https:\u002F\u002Fgithub.com\u002Fcuriousily",[83],{"name":84,"color":85,"percentage":86},"Jupyter Notebook","#DA5B0B",100,875,278,"2026-04-18T19:57:21","MIT","Linux, macOS, Windows","非必需（支持本地运行 Ollama 及云端部署）；若进行 LLM 微调或本地大模型推理，建议 NVIDIA GPU 且显存 8GB+（QLoRA 可降低需求），具体 CUDA 版本未说明","最低 8GB，推荐 16GB+（视模型大小及数据处理量而定）",{"notes":95,"python":96,"dependencies":97},"本项目为综合性训练营教程，涵盖从基础到生产部署的全流程。支持在 Google Colab 云端运行，也支持本地环境（通过 Ollama 运行模型）。涉及容器化部署需安装 Docker，云端部署需 AWS 账号。部分高级课程（如微调、本地大模型）对硬件有一定要求，但基础课程仅需普通电脑即可运行。","3.8+",[98,99,100,101,102,103,104,105,106,107],"torch","pandas","numpy","scikit-learn","fastapi","docker","langchain","ollama","dvc","mlflow",[14,13,36],[110,111,104,112,113,114,115,116,117,118,105,119,120],"artificial-intelligence","chatgpt","large-language-models","llama","machine-learning","prompt-engineering","rag","crewai","langgraph","ollama-python","ai-agents","2026-03-27T02:49:30.150509","2026-04-20T16:48:18.953585",[],[]]