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translations/ur/.co-op-translator.json

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+    "original_hash": "8d9c5a4a7c7798d699672a22cb7fea86",
+    "translation_date": "2025-10-03T09:36:41+00:00",
+    "source_file": "troubleshoot.md",
+    "language_code": "zh-CN"
+  }
+}

translations/zh-CN/AGENTS.md

@@ -0,0 +1,317 @@
+# AGENTS.md
+
+## 项目概述
+
+AI for Beginners 是一个为期 12 周、共 24 节课的全面课程，涵盖人工智能基础知识。该教育资源库包括使用 Jupyter Notebooks 的实践课程、测验和动手实验。课程内容包括：
+
+- 符号 AI：知识表示与专家系统
+- 使用 TensorFlow 和 PyTorch 的神经网络与深度学习
+- 计算机视觉技术与架构
+- 自然语言处理（NLP），包括 transformers 和 BERT
+- 专题内容：遗传算法、强化学习、多智能体系统
+- AI 伦理与负责任的 AI 原则
+
+**关键技术：** Python 3、Jupyter Notebooks、TensorFlow、PyTorch、Keras、OpenCV、Vue.js（用于测验应用）
+
+**架构：** 教育内容资源库，按主题区域组织的 Jupyter Notebooks，辅以基于 Vue.js 的测验应用和广泛的多语言支持。
+
+## 设置命令
+
+### 主要开发环境（Python/Jupyter）
+
+课程设计基于 Python 和 Jupyter Notebooks 运行。推荐使用 miniconda：
+
+```bash
+# Clone the repository
+git clone https://github.com/microsoft/ai-for-beginners
+cd ai-for-beginners
+
+# Create and activate conda environment
+conda env create --name ai4beg --file environment.yml
+conda activate ai4beg
+
+# Start Jupyter Notebook
+jupyter notebook
+# OR
+jupyter lab
+```
+
+### 替代方案：使用 devcontainer
+
+```bash
+# Open in VS Code and select "Reopen in Container" when prompted
+# The devcontainer will automatically set up the environment
+```
+
+### 测验应用设置
+
+测验应用是一个独立的 Vue.js 应用，位于 `etc/quiz-app/`：
+
+```bash
+cd etc/quiz-app
+npm install
+npm run serve  # Development server
+npm run build  # Production build
+npm run lint   # Lint and fix files
+```
+
+## 开发工作流
+
+### 使用 Jupyter Notebooks
+
+1. **本地开发：**
+   - 激活 conda 环境：`conda activate ai4beg`
+   - 启动 Jupyter：`jupyter notebook` 或 `jupyter lab`
+   - 导航到课程文件夹并打开 `.ipynb` 文件
+   - 交互式运行单元格以跟随课程内容
+
+2. **使用 VS Code 和 Python 扩展：**
+   - 在 VS Code 中打开资源库
+   - 安装 Python 扩展
+   - VS Code 会自动检测并使用 conda 环境
+   - 直接在 VS Code 中打开 `.ipynb` 文件
+
+3. **云端开发：**
+   - **GitHub Codespaces：** 点击“Code” → “Codespaces” → “Create codespace on main”
+   - **Binder：** 使用 README 中的 Binder 徽章在浏览器中启动
+   - 注意：Binder 资源有限且有部分网络访问限制
+
+### 高级课程的 GPU 支持
+
+后续课程显著受益于 GPU 加速：
+
+- **Azure Data Science VM：** 使用支持 GPU 的 NC 系列虚拟机
+- **Azure Machine Learning：** 使用带 GPU 计算的 notebook 功能
+- **Google Colab：** 单独上传 notebooks（提供免费 GPU 支持）
+
+### 测验应用开发
+
+```bash
+cd etc/quiz-app
+npm run serve  # Hot-reload development server at http://localhost:8080
+```
+
+## 测试说明
+
+这是一个以学习内容为重点的教育资源库，而非软件测试，因此没有传统的测试套件。
+
+### 验证方法：
+
+1. **Jupyter Notebooks：** 按顺序执行单元格以验证代码示例是否正常运行
+2. **测验应用测试：** 通过开发服务器进行手动测试
+3. **翻译验证：** 检查 `translations/` 文件夹中的翻译内容
+4. **测验应用代码检查：** 在 `etc/quiz-app/` 中运行 `npm run lint`
+
+### 运行代码示例：
+
+```bash
+# Activate environment first
+conda activate ai4beg
+
+# Run Python scripts directly
+python lessons/4-ComputerVision/07-ConvNets/pytorchcv.py
+
+# Or execute notebooks
+jupyter notebook lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb
+```
+
+## 代码风格
+
+### Python 代码风格
+
+- 遵循教育代码的标准 Python 约定
+- 代码清晰易读，优先考虑学习而非优化
+- 注释解释关键概念
+- 适配 Jupyter Notebook：尽量使单元格自包含
+- 对课程内容没有严格的代码检查要求
+
+### JavaScript/Vue.js（测验应用）
+
+- ESLint 配置位于 `etc/quiz-app/package.json`
+- 运行 `npm run lint` 检查并自动修复问题
+- 遵循 Vue 2.x 约定
+- 基于组件的架构
+
+### 文件组织
+
+```
+lessons/
+  ├── 0-course-setup/          # Setup instructions
+  ├── 1-Intro/                 # Introduction to AI
+  ├── 2-Symbolic/              # Symbolic AI
+  ├── 3-NeuralNetworks/        # Neural Networks basics
+  ├── 4-ComputerVision/        # Computer Vision
+  ├── 5-NLP/                   # Natural Language Processing
+  ├── 6-Other/                 # Other AI techniques
+  ├── 7-Ethics/                # AI Ethics
+  └── X-Extras/                # Additional content
+
+etc/
+  ├── quiz-app/                # Vue.js quiz application
+  └── quiz-src/                # Quiz source files
+
+translations/                  # Multi-language translations
+```
+
+## 构建与部署
+
+### Jupyter 内容
+
+无需构建过程 - Jupyter Notebooks 可直接执行。
+
+### 测验应用
+
+```bash
+cd etc/quiz-app
+
+# Development
+npm run serve
+
+# Production build
+npm run build  # Outputs to etc/quiz-app/dist/
+
+# Deploy to Azure Static Web Apps
+# Azure automatically creates GitHub Actions workflow
+# See etc/quiz-app/README.md for detailed deployment instructions
+```
+
+### 文档站点
+
+该资源库使用 Docsify 进行文档管理：
+- `index.html` 作为入口点
+- 无需构建 - 直接通过 GitHub Pages 提供服务
+- 访问地址：https://microsoft.github.io/AI-For-Beginners/
+
+## 贡献指南
+
+### 拉取请求流程
+
+1. **标题格式：** 清晰、描述性的标题，说明更改内容
+2. **CLA 要求：** 必须签署 Microsoft CLA（自动检查）
+3. **内容指南：**
+   - 保持教育重点和面向初学者的风格
+   - 测试 notebooks 中的所有代码示例
+   - 确保 notebooks 能从头到尾运行
+   - 如果修改英文内容，请更新翻译
+4. **测验应用更改：** 提交前运行 `npm run lint`
+
+### 翻译贡献
+
+- 翻译通过 GitHub Actions 使用 co-op-translator 自动完成
+- 手动翻译存放在 `translations/<language-code>/`
+- 测验翻译存放在 `etc/quiz-app/src/assets/translations/`
+- 支持语言：40+ 种语言（完整列表见 README）
+
+### 活跃贡献领域
+
+请参阅 `etc/CONTRIBUTING.md` 了解当前需求：
+- 深度强化学习部分
+- 目标检测改进
+- 命名实体识别示例
+- 自定义嵌入训练样本
+
+## 环境配置
+
+### 所需依赖项
+
+```bash
+# Core Python packages (from requirements.txt)
+tensorflow==2.17.0
+torch (via conda)
+torchvision (via conda)
+keras==3.5.0
+opencv (via conda)
+scikit-learn
+numpy==1.26
+pandas==2.2.2
+matplotlib==3.9
+jupyter
+```
+
+### 环境变量
+
+基本使用无需特殊环境变量。
+
+对于 Azure 部署（测验应用）：
+- `AZURE_STATIC_WEB_APPS_API_TOKEN`（由 Azure 自动设置）
+
+## 调试与故障排除
+
+### 常见问题
+
+**问题：** Conda 环境创建失败  
+- **解决方案：** 先更新 conda：`conda update conda -y`  
+- 确保磁盘空间充足（建议 50GB）
+
+**问题：** 找不到 Jupyter 内核  
+- **解决方案：**  
+  ```bash
+  conda activate ai4beg
+  python -m ipykernel install --user --name ai4beg
+  ```
+
+**问题：** Notebooks 中未检测到 GPU  
+- **解决方案：**  
+  - 验证 CUDA 安装：`nvidia-smi`  
+  - 检查 PyTorch GPU：`python -c "import torch; print(torch.cuda.is_available())"`  
+  - 检查 TensorFlow GPU：`python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"`
+
+**问题：** 测验应用无法启动  
+- **解决方案：**  
+  ```bash
+  cd etc/quiz-app
+  rm -rf node_modules package-lock.json
+  npm install
+  npm run serve
+  ```
+
+**问题：** Binder 超时或阻止下载  
+- **解决方案：** 使用 GitHub Codespaces 或本地设置以获得更好的资源访问
+
+### 内存问题
+
+某些课程需要大量内存（建议 8GB+）：  
+- 对于资源密集型课程，使用云端虚拟机  
+- 训练模型时关闭其他应用  
+- 如果内存不足，可在 notebooks 中减少批量大小
+
+## 附加说明
+
+### 课程讲师须知
+
+- 请参阅 `lessons/0-course-setup/for-teachers.md` 获取教学指导
+- 课程内容是自包含的，可按顺序教学或单独选择
+- 预计时间：每周 2 节课，共 12 周
+
+### 云资源
+
+- **Azure for Students：** 学生可获得免费额度
+- **Microsoft Learn：** 提供补充学习路径
+- **Binder：** 免费但资源有限，且有部分网络限制
+
+### 代码执行选项
+
+1. **本地（推荐）：** 完全控制，最佳性能，支持 GPU
+2. **GitHub Codespaces：** 基于云的 VS Code，快速访问
+3. **Binder：** 基于浏览器的 Jupyter，免费但有限
+4. **Azure ML Notebooks：** 企业级选项，支持 GPU
+5. **Google Colab：** 单独上传 notebooks，提供免费 GPU 层
+
+### 使用 Notebooks
+
+- Notebooks 设计为逐个单元格运行以便学习
+- 许多 notebooks 在首次运行时会下载数据集（可能需要一些时间）
+- 某些模型训练需要 GPU 才能达到合理的时间
+- 尽可能使用预训练模型以减少计算需求
+
+### 性能注意事项
+
+- 后期的计算机视觉课程（CNNs、GANs）受益于 GPU
+- NLP transformer 课程可能需要大量内存
+- 从头训练是教育性的，但耗时较长
+- 迁移学习示例可最大限度减少训练时间
+
+---
+
+**免责声明**：  
+本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。

translations/zh-CN/README.md

@@ -0,0 +1,224 @@
+[![GitHub license](https://img.shields.io/github/license/microsoft/AI-For-Beginners.svg)](https://github.com/microsoft/AI-For-Beginners/blob/main/LICENSE)
+[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/graphs/contributors/)
+[![GitHub issues](https://img.shields.io/github/issues/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/issues/)
+[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/AI-For-Beginners.svg)](https://GitHub.com/microsoft/AI-For-Beginners/pulls/)
+[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+
+[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/AI-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/AI-For-Beginners/watchers/)
+[![GitHub forks](https://img.shields.io/github/forks/microsoft/AI-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/AI-For-Beginners/network/)
+[![GitHub stars](https://img.shields.io/github/stars/microsoft/AI-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/AI-For-Beginners/stargazers/)
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/microsoft/ai-for-beginners/HEAD)
+[![Gitter](https://badges.gitter.im/Microsoft/ai-for-beginners.svg)](https://gitter.im/Microsoft/ai-for-beginners?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
+
+[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
+
+# 人工智能初学者课程
+
+|![Sketchnote by @girlie_mac https://twitter.com/girlie_mac](../../translated_images/zh-CN/ai-overview.0857791951d19500.webp)|
+|:---:|
+| 人工智能初学者 - _草图笔记由 [@girlie_mac](https://twitter.com/girlie_mac) 提供_ |
+
+通过我们的为期12周、共24课的课程，探索**人工智能**（AI）的世界！课程包含实用的课程、测验和实验室。课程面向初学者，涵盖了TensorFlow和PyTorch等工具，以及人工智能伦理。
+
+### 🌐 多语言支持
+
+#### 通过 GitHub Actions 支持（自动且始终保持最新）
+
+<!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
+[阿拉伯语](../ar/README.md) | [孟加拉语](../bn/README.md) | [保加利亚语](../bg/README.md) | [缅甸语](../my/README.md) | [中文（简体）](./README.md) | [中文（繁体，香港）](../zh-HK/README.md) | [中文（繁体，澳门）](../zh-MO/README.md) | [中文（繁体，台湾）](../zh-TW/README.md) | [克罗地亚语](../hr/README.md) | [捷克语](../cs/README.md) | [丹麦语](../da/README.md) | [荷兰语](../nl/README.md) | [爱沙尼亚语](../et/README.md) | [芬兰语](../fi/README.md) | [法语](../fr/README.md) | [德语](../de/README.md) | [希腊语](../el/README.md) | [希伯来语](../he/README.md) | [印地语](../hi/README.md) | [匈牙利语](../hu/README.md) | [印度尼西亚语](../id/README.md) | [意大利语](../it/README.md) | [日语](../ja/README.md) | [卡纳达语](../kn/README.md) | [韩语](../ko/README.md) | [立陶宛语](../lt/README.md) | [马来语](../ms/README.md) | [马拉雅拉姆语](../ml/README.md) | [马拉地语](../mr/README.md) | [尼泊尔语](../ne/README.md) | [尼日利亚皮钦语](../pcm/README.md) | [挪威语](../no/README.md) | [波斯语（法尔西语）](../fa/README.md) | [波兰语](../pl/README.md) | [巴西葡萄牙语](../pt-BR/README.md) | [葡萄牙语（葡萄牙）](../pt-PT/README.md) | [旁遮普语（古鲁穆奇）](../pa/README.md) | [罗马尼亚语](../ro/README.md) | [俄语](../ru/README.md) | [塞尔维亚语（西里尔字母）](../sr/README.md) | [斯洛伐克语](../sk/README.md) | [斯洛文尼亚语](../sl/README.md) | [西班牙语](../es/README.md) | [斯瓦希里语](../sw/README.md) | [瑞典语](../sv/README.md) | [塔加洛语（菲律宾语）](../tl/README.md) | [泰米尔语](../ta/README.md) | [泰卢固语](../te/README.md) | [泰语](../th/README.md) | [土耳其语](../tr/README.md) | [乌克兰语](../uk/README.md) | [乌尔都语](../ur/README.md) | [越南语](../vi/README.md)
+
+> **更喜欢本地克隆？**
+
+> 本仓库包含超过50种语言的翻译，这大大增加了下载大小。若想不带翻译进行克隆，请使用稀疏检出：
+> ```bash
+> git clone --filter=blob:none --sparse https://github.com/microsoft/AI-For-Beginners.git
+> cd AI-For-Beginners
+> git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'
+> ```
+> 这将为您提供完成课程所需的所有内容，且下载速度更快。
+<!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
+
+**如果您希望支持其他翻译语言，请参阅[这里](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
+
+## 加入社区
+[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
+
+## 您将学到的内容
+
+**[课程思维导图](http://soshnikov.com/courses/ai-for-beginners/mindmap.html)**
+
+在本课程中，您将学习：
+
+* 人工智能的不同方法，包括带有**知识表示**和推理的“传统”符号方法（[GOFAI](https://en.wikipedia.org/wiki/Symbolic_artificial_intelligence)）。
+* 位于现代人工智能核心的**神经网络**和**深度学习**。我们将使用两个最流行的框架——[TensorFlow](http://Tensorflow.org)和[PyTorch](http://pytorch.org)中的代码，来说明这些重要主题背后的概念。
+* 处理图像和文本的**神经架构**。我们将涵盖近期模型，但可能对最新前沿应用涉及较少。
+* 较少流行的AI方法，如**遗传算法**和**多智能体系统**。
+
+本课程不涵盖的内容：
+
+> [在我们的 Microsoft Learn 资源集中找到本课程的所有额外资源](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)
+
+* **企业中的 AI** 业务案例。您可以考虑参加 Microsoft Learn 上的[面向业务用户的 AI 入门](https://docs.microsoft.com/learn/paths/introduction-ai-for-business-users/?WT.mc_id=academic-77998-bethanycheum)学习路径，或由[INSEAD](https://www.insead.edu/)合作开发的[AI 商业学院](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-bethanycheum)。
+* 我们[机器学习初学者课程](http://github.com/Microsoft/ML-for-Beginners)中详细介绍的**经典机器学习**。
+* 使用**[认知服务](https://azure.microsoft.com/services/cognitive-services/?WT.mc_id=academic-77998-bethanycheum)**构建的实用AI应用。为此，我们建议您从 Microsoft Learn 上的[视觉](https://docs.microsoft.com/learn/paths/create-computer-vision-solutions-azure-cognitive-services/?WT.mc_id=academic-77998-bethanycheum)、[自然语言处理](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-bethanycheum)、**[基于 Azure OpenAI 服务的生成式 AI](https://learn.microsoft.com/en-us/training/paths/develop-ai-solutions-azure-openai/?WT.mc_id=academic-77998-bethanycheum)**等模块开始。
+* 特定的机器学习**云框架**，如[Azure 机器学习](https://azure.microsoft.com/services/machine-learning/?WT.mc_id=academic-77998-bethanycheum)、[Microsoft Fabric](https://learn.microsoft.com/en-us/training/paths/get-started-fabric/?WT.mc_id=academic-77998-bethanycheum)或[Azure Databricks](https://docs.microsoft.com/learn/paths/data-engineer-azure-databricks?WT.mc_id=academic-77998-bethanycheum)。可考虑使用[使用 Azure 机器学习构建和运营机器学习解决方案](https://docs.microsoft.com/learn/paths/build-ai-solutions-with-azure-ml-service/?WT.mc_id=academic-77998-bethanycheum)和[使用 Azure Databricks 构建和运营机器学习解决方案](https://docs.microsoft.com/learn/paths/build-operate-machine-learning-solutions-azure-databricks/?WT.mc_id=academic-77998-bethanycheum)学习路径。
+* **对话式 AI** 和 **聊天机器人**。微软提供了独立的[创建对话式 AI 解决方案](https://docs.microsoft.com/learn/paths/create-conversational-ai-solutions/?WT.mc_id=academic-77998-bethanycheum)学习路径，您也可以参考[这篇博客文章](https://soshnikov.com/azure/hello-bot-conversational-ai-on-microsoft-platform/)了解更多细节。
+* 深度学习背后的**深层数学**。为此，我们推荐 Ian Goodfellow、Yoshua Bengio 和 Aaron Courville 的著作[深度学习](https://www.amazon.com/Deep-Learning-Adaptive-Computation-Machine/dp/0262035618)，该书也可在线阅读：[https://www.deeplearningbook.org/](https://www.deeplearningbook.org/)。
+
+对于 _云端 AI_ 主题的入门，可以考虑参加[Azure 人工智能入门](https://docs.microsoft.com/learn/paths/get-started-with-artificial-intelligence-on-azure/?WT.mc_id=academic-77998-bethanycheum)学习路径。
+
+# 内容
+
+|     |                                                                 课程链接                                                                 |                                           PyTorch/Keras/TensorFlow                                          | 实验                                                           |
+| :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ |
+| 0  |                                 [课程设置](./lessons/0-course-setup/setup.md)                                 |                      [设置您的开发环境](./lessons/0-course-setup/how-to-run.md)                       |   |
+| I  |               [**人工智能入门**](./lessons/1-Intro/README.md)      | | |
+| 01  |       [人工智能简介及历史](./lessons/1-Intro/README.md)       |           -                            | -  |
+| II |              **符号 AI**              |
+| 02  |       [知识表示与专家系统](./lessons/2-Symbolic/README.md)       |            [专家系统](./lessons/2-Symbolic/Animals.ipynb) /  [本体](./lessons/2-Symbolic/FamilyOntology.ipynb) /[概念图](./lessons/2-Symbolic/MSConceptGraph.ipynb)                             |  |
+| III |                        [**神经网络简介**](./lessons/3-NeuralNetworks/README.md) |||
+| 03  |                [感知器](./lessons/3-NeuralNetworks/03-Perceptron/README.md)                 |                       [笔记本](./lessons/3-NeuralNetworks/03-Perceptron/Perceptron.ipynb)                      | [实验](./lessons/3-NeuralNetworks/03-Perceptron/lab/README.md) |
+| 04  |                   [多层感知器与创建我们自己的框架](./lessons/3-NeuralNetworks/04-OwnFramework/README.md)                   |        [笔记本](./lessons/3-NeuralNetworks/04-OwnFramework/OwnFramework.ipynb)        | [实验](./lessons/3-NeuralNetworks/04-OwnFramework/lab/README.md) |
+| 05  |            [框架简介 (PyTorch/TensorFlow) 与过拟合](./lessons/3-NeuralNetworks/05-Frameworks/README.md)             |           [PyTorch](./lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb) / [Keras](./lessons/3-NeuralNetworks/05-Frameworks/IntroKeras.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb)             | [实验](./lessons/3-NeuralNetworks/05-Frameworks/lab/README.md) |
+| IV  |            [**计算机视觉**](./lessons/4-ComputerVision/README.md)             | [PyTorch](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste) / [TensorFlow](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)| [微软 Azure 上的计算机视觉探索](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) |
+| 06  |            [计算机视觉简介. OpenCV](./lessons/4-ComputerVision/06-IntroCV/README.md)             |           [笔记本](./lessons/4-ComputerVision/06-IntroCV/OpenCV.ipynb)         | [实验](./lessons/4-ComputerVision/06-IntroCV/lab/README.md) |
+| 07  |            [卷积神经网络](./lessons/4-ComputerVision/07-ConvNets/README.md) &  [CNN 架构](./lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md)             |           [PyTorch](./lessons/4-ComputerVision/07-ConvNets/ConvNetsPyTorch.ipynb) /[TensorFlow](./lessons/4-ComputerVision/07-ConvNets/ConvNetsTF.ipynb)             | [实验](./lessons/4-ComputerVision/07-ConvNets/lab/README.md) |
+| 08  |            [预训练网络与迁移学习](./lessons/4-ComputerVision/08-TransferLearning/README.md) 和 [训练技巧](./lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md)             |           [PyTorch](./lessons/4-ComputerVision/08-TransferLearning/TransferLearningPyTorch.ipynb) / [TensorFlow](./lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.ipynb)             | [实验](./lessons/4-ComputerVision/08-TransferLearning/lab/README.md) |
+| 09  |            [自编码器与变分自编码器](./lessons/4-ComputerVision/09-Autoencoders/README.md)             |           [PyTorch](./lessons/4-ComputerVision/09-Autoencoders/AutoEncodersPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/09-Autoencoders/AutoencodersTF.ipynb)             |  |
+| 10  |            [生成对抗网络与艺术风格迁移](./lessons/4-ComputerVision/10-GANs/README.md)             |           [PyTorch](./lessons/4-ComputerVision/10-GANs/GANPyTorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/10-GANs/GANTF.ipynb)             |  |
+| 11  |            [目标检测](./lessons/4-ComputerVision/11-ObjectDetection/README.md)             |         [TensorFlow](./lessons/4-ComputerVision/11-ObjectDetection/ObjectDetection.ipynb)             | [实验](./lessons/4-ComputerVision/11-ObjectDetection/lab/README.md) |
+| 12  |            [语义分割. U-Net](./lessons/4-ComputerVision/12-Segmentation/README.md)             |           [PyTorch](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationPytorch.ipynb) / [TensorFlow](./lessons/4-ComputerVision/12-Segmentation/SemanticSegmentationTF.ipynb)             |  |
+| V  |            [**自然语言处理**](./lessons/5-NLP/README.md)             | [PyTorch](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste) /[TensorFlow](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste) | [微软 Azure 上的自然语言处理探索](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum)|
+| 13  |            [文本表示. 词袋模型/TF-IDF](./lessons/5-NLP/13-TextRep/README.md)             |           [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/TextRepresentationTF.ipynb)             | |
+| 14  |            [语义词嵌入. Word2Vec 和 GloVe](./lessons/5-NLP/14-Embeddings/README.md)             |           [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/EmbeddingsTF.ipynb)             |  |
+| 15  |            [语言模型. 训练你自己的嵌入](./lessons/5-NLP/15-LanguageModeling/README.md)             |           [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-PyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling/CBoW-TF.ipynb)             | [实验](./lessons/5-NLP/15-LanguageModeling/lab/README.md) |
+| 16  |            [循环神经网络](./lessons/5-NLP/16-RNN/README.md)             |           [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNPyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/RNNTF.ipynb)             |  |
+| 17  |            [生成式循环网络](./lessons/5-NLP/17-GenerativeNetworks/README.md)             |           [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativePyTorch.ipynb) / [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/GenerativeTF.ipynb)             | [实验](./lessons/5-NLP/17-GenerativeNetworks/lab/README.md) |
+| 18  |            [变压器模型. BERT](./lessons/5-NLP/18-Transformers/README.md)             |           [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersPyTorch.ipynb) /[TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/TransformersTF.ipynb)             |  |
+| 19  |            [命名实体识别](./lessons/5-NLP/19-NER/README.md)             |           [TensorFlow](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/19-NER/NER-TF.ipynb)             | [实验](./lessons/5-NLP/19-NER/lab/README.md) |
+| 20  |            [大型语言模型，提示编程与少样本任务](./lessons/5-NLP/20-LangModels/README.md)             |           [PyTorch](https://microsoft.github.io/AI-For-Beginners/lessons/5-NLP/20-LangModels/GPT-PyTorch.ipynb) | |
+| VI |            **其他 AI 技术** || |
+| 21  |            [遗传算法](./lessons/6-Other/21-GeneticAlgorithms/README.md)             |           [笔记本](./lessons/6-Other/21-GeneticAlgorithms/Genetic.ipynb) | |
+| 22  |            [深度强化学习](./lessons/6-Other/22-DeepRL/README.md)             |           [PyTorch](./lessons/6-Other/22-DeepRL/CartPole-RL-PyTorch.ipynb) /[TensorFlow](./lessons/6-Other/22-DeepRL/CartPole-RL-TF.ipynb)             | [实验](./lessons/6-Other/22-DeepRL/lab/README.md) |
+| 23  |            [多智能体系统](./lessons/6-Other/23-MultiagentSystems/README.md)             |  | |
+| VII |            **AI 伦理** | | |
+| 24  |            [AI 伦理与负责任的 AI](./lessons/7-Ethics/README.md)             |           [Microsoft Learn：负责任的 AI 原则](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste) | |
+| IX  |            **额外内容** | | |
+| 25  |            [多模态网络，CLIP 和 VQGAN](./lessons/X-Extras/X1-MultiModal/README.md)             |           [笔记本](./lessons/X-Extras/X1-MultiModal/Clip.ipynb)    | |
+
+## 每节课包含
+
+* 预习材料
+* 可执行的 Jupyter 笔记本，通常针对特定框架（**PyTorch** 或 **TensorFlow**）。可执行的笔记本还包含大量理论内容，因此要理解主题，至少需要阅读其中一个版本的笔记本（PyTorch 或 TensorFlow）。
+* 某些主题提供 **实验**，让你有机会尝试将所学知识应用到具体问题中。
+* 部分章节包含指向相关主题的 [**微软学习**](https://learn.microsoft.com/en-us/collections/7w28iy2xrqzdj0?WT.mc_id=academic-77998-bethanycheum) 模块链接。
+
+## 入门指南
+
+### 🎯 AI 新手？从这里开始！
+
+如果你刚接触 AI，想要快速实践示例，请查看我们的 [**初学者友好示例**](./examples/README.md)！包括：
+
+- 🌟 **Hello AI 世界** - 你的第一个 AI 程序（模式识别）
+- 🧠 **简单神经网络** - 从零构建神经网络  
+
+- 🖼️ **图像分类器** - 通过详细注释对图像进行分类
+- 💬 **文本情感分析** - 分析文本的正面/负面情绪
+
+这些示例旨在帮助你在深入完整课程之前理解 AI 概念。
+
+### 📚 完整课程设置
+
+- 我们创建了一个 [设置课程](./lessons/0-course-setup/setup.md) 来帮助你搭建开发环境。- 对于教师，我们也创建了一个[课程设置课程](./lessons/0-course-setup/for-teachers.md)！
+- 如何在 VSCode 或 Codespace 中[运行代码](./lessons/0-course-setup/how-to-run.md)
+
+请按照以下步骤操作：
+
+Fork 代码库：点击本页右上角的“Fork”按钮。
+
+克隆代码库：`git clone https://github.com/microsoft/AI-For-Beginners.git`
+
+别忘了给该仓库点星 (🌟)，方便以后查找。
+
+## 认识其他学习者
+
+加入我们的[官方 AI Discord 服务器](https://aka.ms/genai-discord?WT.mc_id=academic-105485-bethanycheum)，结识并与其他参加本课程的学习者交流，获得支持。
+
+如果你在构建过程中有产品反馈或疑问，请访问我们的[Azure AI Foundry 开发者论坛](https://aka.ms/foundry/forum)
+
+## 测验
+
+> **关于测验的说明**：所有测验均包含在 etc\quiz-app 下的 Quiz-app 文件夹中，或在[此处在线查看](https://ff-quizzes.netlify.app/)。它们链接于课程内，测验应用可以本地运行或部署到 Azure；请遵循 `quiz-app` 文件夹中的说明。测验内容正逐步实现本地化。
+
+## 需要帮助
+
+你有建议或发现了拼写或代码错误吗？请提交问题或创建拉取请求。
+
+## 特别感谢
+
+* **✍️ 主要作者：** [Dmitry Soshnikov](http://soshnikov.com), 博士
+* **🔥 编辑：** [Jen Looper](https://twitter.com/jenlooper), 博士
+* **🎨 草图插画师：** [Tomomi Imura](https://twitter.com/girlie_mac)
+* **✅ 测验创建者：** [Lateefah Bello](https://github.com/CinnamonXI), [MLSA](https://studentambassadors.microsoft.com/)
+* **🙏 核心贡献者：** [Evgenii Pishchik](https://github.com/Pe4enIks)
+
+## 其他课程
+
+我们的团队还制作了其他课程！查看：
+
+<!-- CO-OP TRANSLATOR OTHER COURSES START -->
+### LangChain
+[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
+[![LangChain.js for Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
+
+---
+
+### Azure / Edge / MCP / Agents
+[![AZD for Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![Edge AI for Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![MCP for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![AI Agents for Beginners](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
+
+---
+
+### 生成式 AI 系列
+[![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
+[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
+[![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
+
+---
+
+### 核心学习
+[![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
+[![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
+[![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
+[![Cybersecurity for Beginners](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
+[![Web Dev for Beginners](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
+[![IoT for Beginners](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
+[![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
+
+---
+
+### Copilot 系列
+[![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
+[![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
+[![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
+<!-- CO-OP TRANSLATOR OTHER COURSES END -->
+
+## 获取帮助
+
+如果你遇到困难或有任何关于构建 AI 应用的问题，请加入 MCP 学习者和经验丰富开发者的讨论。这是一个支持性的社区，欢迎提问并自由分享知识。
+
+[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
+
+如果你在构建过程中有产品反馈或错误，请访问：
+
+[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
+
+---
+
+<!-- CO-OP TRANSLATOR DISCLAIMER START -->
+**免责声明**：
+本文件由 AI 翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。虽然我们努力确保准确性，但请注意自动翻译可能存在错误或不准确之处。请以原始语言的原文档为权威来源。对于关键信息，建议采用专业人工翻译。因使用本翻译而产生的任何误解或误释，我们概不负责。
+<!-- CO-OP TRANSLATOR DISCLAIMER END -->

translations/zh-CN/SECURITY.md

@@ -0,0 +1,40 @@
+## 安全性
+
+Microsoft 非常重视我们软件产品和服务的安全性，这包括通过我们的 GitHub 组织管理的所有源代码库，这些组织包括 [Microsoft](https://github.com/Microsoft)、[Azure](https://github.com/Azure)、[DotNet](https://github.com/dotnet)、[AspNet](https://github.com/aspnet)、[Xamarin](https://github.com/xamarin) 和 [我们的 GitHub 组织](https://opensource.microsoft.com/)。
+
+如果您认为在任何 Microsoft 拥有的代码库中发现了符合 [Microsoft 对安全漏洞的定义](https://aka.ms/opensource/security/definition) 的安全漏洞，请按照以下描述向我们报告。
+
+## 报告安全问题
+
+**请不要通过公共 GitHub 问题报告安全漏洞。**
+
+相反，请通过 Microsoft 安全响应中心 (MSRC) 报告，网址为 [https://msrc.microsoft.com/create-report](https://aka.ms/opensource/security/create-report)。
+
+如果您更愿意在不登录的情况下提交，请发送电子邮件至 [secure@microsoft.com](mailto:secure@microsoft.com)。如果可能，请使用我们的 PGP 密钥加密您的消息；您可以从 [Microsoft 安全响应中心 PGP 密钥页面](https://aka.ms/opensource/security/pgpkey) 下载密钥。
+
+您应该会在 24 小时内收到回复。如果由于某种原因未收到，请通过电子邮件跟进以确保我们收到了您的原始消息。更多信息请访问 [microsoft.com/msrc](https://aka.ms/opensource/security/msrc)。
+
+请尽可能提供以下所需信息，以帮助我们更好地理解问题的性质和范围：
+
+  * 问题类型（例如，缓冲区溢出、SQL 注入、跨站脚本攻击等）
+  * 与问题表现相关的源文件的完整路径
+  * 受影响源代码的位置（标签/分支/提交或直接 URL）
+  * 复现问题所需的任何特殊配置
+  * 复现问题的分步说明
+  * 概念验证或漏洞利用代码（如果可能）
+  * 问题的影响，包括攻击者可能如何利用该问题
+
+这些信息将帮助我们更快地对您的报告进行分类和处理。
+
+如果您是为漏洞赏金计划报告问题，更完整的报告可能会有助于获得更高的赏金奖励。有关我们当前计划的更多详细信息，请访问 [Microsoft 漏洞赏金计划](https://aka.ms/opensource/security/bounty) 页面。
+
+## 首选语言
+
+我们更倾向于使用英语进行所有交流。
+
+## 政策
+
+Microsoft 遵循 [协调漏洞披露](https://aka.ms/opensource/security/cvd) 原则。
+
+**免责声明**：  
+本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原文档的原始语言版本为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而引起的任何误解或误读不承担责任。

translations/zh-CN/etc/CODE_OF_CONDUCT.md

@@ -0,0 +1,12 @@
+# Microsoft 开源行为准则
+
+本项目已采用 [Microsoft 开源行为准则](https://opensource.microsoft.com/codeofconduct/)。
+
+资源：
+
+- [Microsoft 开源行为准则](https://opensource.microsoft.com/codeofconduct/)
+- [Microsoft 行为准则常见问题](https://opensource.microsoft.com/codeofconduct/faq/)
+- 如有疑问或需帮助，请联系 [opencode@microsoft.com](mailto:opencode@microsoft.com)
+
+**免责声明**：  
+本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于重要信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。

translations/zh-CN/etc/CONTRIBUTING.md

@@ -0,0 +1,22 @@
+# 贡献
+
+本项目欢迎贡献和建议。大多数贡献需要您同意贡献者许可协议 (CLA)，声明您拥有权利并实际授予我们使用您的贡献的权利。详情请访问 https://cla.microsoft.com。
+
+当您提交拉取请求时，CLA-bot 会自动判断您是否需要提供 CLA，并适当地标记 PR（例如，标签、评论）。只需按照机器人提供的指示操作即可。您只需在所有使用我们 CLA 的代码库中完成一次此操作。
+
+本项目已采用 [Microsoft 开源行为准则](https://opensource.microsoft.com/codeofconduct/)。  
+有关更多信息，请参阅 [行为准则常见问题](https://opensource.microsoft.com/codeofconduct/faq/)  
+或通过 [opencode@microsoft.com](mailto:opencode@microsoft.com) 联系我们，提出其他问题或意见。
+
+# 寻求贡献
+
+我们目前正在积极寻找以下主题的贡献：
+
+- [ ] 撰写关于深度强化学习的章节  
+- [ ] 改进关于目标检测的章节和笔记本  
+- [ ] PyTorch Lightning（针对[此章节](https://github.com/microsoft/AI-For-Beginners/blob/main/3-NeuralNetworks/05-Frameworks/README.md)）  
+- [ ] 撰写关于命名实体识别的章节和示例  
+- [ ] 为[此章节](https://github.com/microsoft/AI-For-Beginners/tree/main/5-NLP/15-LanguageModeling)创建训练自定义嵌入的示例  
+
+**免责声明**：  
+本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。我们对于因使用本翻译而引起的任何误解或误读不承担责任。

translations/zh-CN/etc/Mindmap.md

@@ -0,0 +1,78 @@
+# 人工智能
+
+## [人工智能简介](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/1-Intro/README.md)
+ - 人工智能定义
+ - 人工智能历史
+ - 人工智能方法
+     - 自上而下/符号主义
+     - 自下而上/神经网络
+     - 演化方法
+     - 协同/涌现人工智能
+ - [微软人工智能商业学院](https://www.microsoft.com/ai/ai-business-school/?WT.mc_id=academic-77998-cacaste)
+
+## [符号主义人工智能](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/README.md)
+ - 知识表示
+ - [专家系统](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/Animals.ipynb)
+ - [本体论](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/2-Symbolic/FamilyOntology.ipynb)
+ - 语义网
+
+## [神经网络](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/README.md)
+ - [感知机](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/03-Perceptron/README.md)
+ - [多层网络](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/04-OwnFramework/README.md)
+ - [框架简介](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/05-Frameworks/README.md)
+   - [PyTorch](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/05-Frameworks/IntroPyTorch.ipynb)
+   - [TensorFlow](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/05-Frameworks/IntroKerasTF.md)
+   - [过拟合](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/3-NeuralNetworks/05-Frameworks/Overfitting.md)
+
+## [计算机视觉](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/README.md)
+ - 在微软学习平台
+    - [人工智能基础：探索计算机视觉](https://docs.microsoft.com/learn/paths/explore-computer-vision-microsoft-azure/?WT.mc_id=academic-77998-cacaste)
+    - [使用 PyTorch 的计算机视觉](https://docs.microsoft.com/learn/modules/intro-computer-vision-pytorch/?WT.mc_id=academic-77998-cacaste)
+    - [使用 TensorFlow 的计算机视觉](https://docs.microsoft.com/learn/modules/intro-computer-vision-TensorFlow/?WT.mc_id=academic-77998-cacaste)
+ - [计算机视觉简介：OpenCV](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/06-IntroCV/README.md)
+ - [卷积网络](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/07-ConvNets/README.md)
+   - [卷积神经网络架构](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/07-ConvNets/CNN_Architectures.md)
+ - [迁移学习](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/08-TransferLearning/README.md)
+   - [训练技巧](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/08-TransferLearning/TrainingTricks.md)
+ - [自动编码器和变分自动编码器](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/09-Autoencoders/README.md)
+ - [生成对抗网络](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/10-GANs/README.md)
+ - [风格迁移](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/10-GANs/StyleTransfer.ipynb)
+ - [目标检测](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/11-ObjectDetection/README.md)
+ - [图像分割](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/4-ComputerVision/12-Segmentation/README.md)
+ 
+## [自然语言处理](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/README.md)
+ - 在微软学习平台
+    - [人工智能基础：探索自然语言处理](https://docs.microsoft.com/learn/paths/explore-natural-language-processing/?WT.mc_id=academic-77998-cacaste)
+    - [使用 PyTorch 的自然语言处理](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-pytorch/?WT.mc_id=academic-77998-cacaste)
+    - [使用 TensorFlow 的自然语言处理](https://docs.microsoft.com/learn/modules/intro-natural-language-processing-TensorFlow/?WT.mc_id=academic-77998-cacaste)
+ - [文本表示](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/13-TextRep/README.md)
+    - 词袋模型
+    - TF/IDF
+ - [语义嵌入](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/14-Embeddings/README.md)
+    - Word2Vec
+    - GloVE
+ - [语言建模](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/15-LanguageModeling)
+ - [循环神经网络](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/16-RNN/README.md)
+     - LSTM
+     - GRU
+ - [生成式循环网络](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/17-GenerativeNetworks/README.md)
+ - [Transformer 和 BERT](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/18-Transformers/README.md)
+ - [命名实体识别](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/19-NER/README.md)
+ - [文本生成与 GPT](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/5-NLP/20-LanguageModels/README.md)
+
+## 其他技术
+ - [遗传算法](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/6-Other/21-GeneticAlgorithms/README.md)
+ - [深度强化学习](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/6-Other/22-DeepRL/README.md)
+ - [多智能体系统](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/6-Other/23-MultiagentSystems/README.md)
+
+## [人工智能伦理](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/7-Ethics/README.md)
+ - [微软学习平台上的负责任人工智能](https://docs.microsoft.com/learn/paths/responsible-ai-business-principles/?WT.mc_id=academic-77998-cacaste)
+
+## 额外内容
+ - [多模态网络](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/X-Extras/X1-MultiModal/README.md)
+   - [CLIP](https://github.com/microsoft/AI-For-Beginners/blob/main/lessons/X-Extras/X1-MultiModal/Clip.ipynb)
+   - DALL-E
+   - VQ-GAN
+
+**免责声明**：  
+本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原文档的原始语言版本为权威来源。对于关键信息，建议使用专业人工翻译。我们对于因使用本翻译而引起的任何误解或误读不承担责任。

translations/zh-CN/etc/SUPPORT.md

@@ -0,0 +1,14 @@
+# 支持
+
+## 如何提交问题和获取帮助  
+
+此项目使用 GitHub Issues 来跟踪错误和功能请求。在提交新问题之前，请先搜索现有问题以避免重复。对于新问题，请将您的错误或功能请求作为新问题提交。
+
+如果需要帮助或对使用此项目有疑问，请使用讨论板。
+
+## Microsoft 支持政策  
+
+对此项目的支持仅限于上述列出的资源。
+
+**免责声明**：  
+本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原文档的原始语言版本为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而引起的任何误解或误读不承担责任。

translations/zh-CN/etc/TRANSLATIONS.md

@@ -0,0 +1,36 @@
+# 通过翻译课程内容贡献
+
+我们欢迎对本课程内容进行翻译！
+
+## 指南
+
+每个课程文件夹和课程介绍文件夹中都有包含翻译后的 Markdown 文件的子文件夹。
+
+> 注意，请不要翻译代码示例文件中的任何代码；唯一需要翻译的内容是 README、作业和测验。谢谢！
+
+翻译后的文件应遵循以下命名规范：
+
+**README._[language]_.md**
+
+其中 _[language]_ 是遵循 ISO 639-1 标准的两位语言缩写（例如，西班牙语为 `README.es.md`，荷兰语为 `README.nl.md`）。
+
+**assignment._[language]_.md**
+
+与 README 文件类似，请翻译作业文件。
+
+**测验**
+
+1. 将您的翻译添加到测验应用中，通过在以下位置添加文件：https://github.com/microsoft/AI-For-Beginners/tree/main/etc/quiz-app/src/assets/translations，并遵循正确的命名规范（例如 en.json, fr.json）。**请不要翻译 'true' 或 'false' 这两个词，谢谢！**
+
+2. 在测验应用的 App.vue 文件中添加您的语言代码到下拉菜单中。
+
+3. 编辑测验应用的 [translations index.js 文件](https://github.com/microsoft/AI-For-Beginners/blob/main/etc/quiz-app/src/assets/translations/index.js)，以添加您的语言。
+
+4. 最后，编辑您翻译后的 README.md 文件中的所有测验链接，使其直接指向您的翻译版测验：https://red-field-0a6ddfd03.1.azurestaticapps.net/quiz/1 改为 https://red-field-0a6ddfd03.1.azurestaticapps.net/quiz/1?loc=id
+
+**感谢您！**
+
+我们真诚地感谢您的努力！
+
+**免责声明**：  
+本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。我们对于因使用本翻译而引起的任何误解或误读不承担责任。

translations/zh-CN/etc/quiz-app/README.md

@@ -0,0 +1,128 @@
+# 测验
+
+这些测验是 AI 课程（https://aka.ms/ai-beginners）的课前和课后测验。
+
+## 添加翻译后的测验集
+
+通过在 `assets/translations` 文件夹中创建相应的测验结构来添加测验翻译。原始测验位于 `assets/translations/en` 文件夹中。测验按照课程分为多个组别。请确保编号与正确的测验部分对齐。整个课程共有 40 个测验，编号从 0 开始。
+
+编辑翻译内容后，修改翻译文件夹中的 `index.js` 文件，按照 `en` 文件夹中的约定导入所有文件。
+
+接着，编辑 `assets/translations` 文件夹中的 `index.js` 文件，导入新翻译的文件。
+
+然后，编辑此应用中的 `App.vue` 文件中的下拉菜单，添加您的语言。确保本地化缩写与您的语言文件夹名称匹配。
+
+最后，编辑翻译课程中的所有测验链接（如果存在），将本地化作为查询参数添加，例如：`?loc=fr`。
+
+## 项目设置
+
+```
+npm install
+```
+
+### 编译并热加载用于开发
+
+```
+npm run serve
+```
+
+### 编译并压缩用于生产
+
+```
+npm run build
+```
+
+### 检查并修复文件
+
+```
+npm run lint
+```
+
+### 自定义配置
+
+请参阅 [配置参考](https://cli.vuejs.org/config/)。
+
+致谢：感谢此测验应用的原始版本：https://github.com/arpan45/simple-quiz-vue
+
+## 部署到 Azure
+
+以下是帮助您入门的分步指南：
+
+1. Fork GitHub 仓库  
+确保您的静态 Web 应用代码在 GitHub 仓库中。Fork 此仓库。
+
+2. 创建 Azure 静态 Web 应用  
+- 创建一个 [Azure 账户](http://azure.microsoft.com)  
+- 访问 [Azure 门户](https://portal.azure.com)  
+- 点击“创建资源”，搜索“静态 Web 应用”。  
+- 点击“创建”。
+
+3. 配置静态 Web 应用  
+- 基本信息：  
+  - 订阅：选择您的 Azure 订阅。  
+  - 资源组：创建一个新的资源组或使用现有的资源组。  
+  - 名称：为您的静态 Web 应用提供一个名称。  
+  - 区域：选择离您的用户最近的区域。
+
+- #### 部署详情：  
+  - 源：选择“GitHub”。  
+  - GitHub 账户：授权 Azure 访问您的 GitHub 账户。  
+  - 组织：选择您的 GitHub 组织。  
+  - 仓库：选择包含静态 Web 应用的仓库。  
+  - 分支：选择您要部署的分支。
+
+- #### 构建详情：  
+  - 构建预设：选择您的应用所使用的框架（例如 React、Angular、Vue 等）。  
+  - 应用位置：指定包含应用代码的文件夹（例如，如果在根目录，则为 /）。  
+  - API 位置：如果有 API，请指定其位置（可选）。  
+  - 输出位置：指定生成的构建输出所在的文件夹（例如 build 或 dist）。
+
+4. 审核并创建  
+审核您的设置并点击“创建”。Azure 将设置必要的资源，并在您的仓库中创建一个 GitHub Actions 工作流。
+
+5. GitHub Actions 工作流  
+Azure 会自动在您的仓库中创建一个 GitHub Actions 工作流文件（.github/workflows/azure-static-web-apps-<name>.yml）。此工作流将处理构建和部署过程。
+
+6. 监控部署  
+前往 GitHub 仓库中的“Actions”标签页。  
+您应该会看到一个正在运行的工作流。此工作流将构建并部署您的静态 Web 应用到 Azure。  
+工作流完成后，您的应用将在提供的 Azure URL 上上线。
+
+### 示例工作流文件
+
+以下是 GitHub Actions 工作流文件的示例：  
+name: Azure Static Web Apps CI/CD  
+```
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    types: [opened, synchronize, reopened, closed]
+    branches:
+      - main
+
+jobs:
+  build_and_deploy_job:
+    runs-on: ubuntu-latest
+    name: Build and Deploy Job
+    steps:
+      - uses: actions/checkout@v2
+      - name: Build And Deploy
+        id: builddeploy
+        uses: Azure/static-web-apps-deploy@v1
+        with:
+          azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN }}
+          repo_token: ${{ secrets.GITHUB_TOKEN }}
+          action: "upload"
+          app_location: "etc/quiz-app # App source code path"
+          api_location: ""API source code path optional
+          output_location: "dist" #Built app content directory - optional
+```
+
+### 其他资源
+- [Azure 静态 Web 应用文档](https://learn.microsoft.com/azure/static-web-apps/getting-started)  
+- [GitHub Actions 文档](https://docs.github.com/actions/use-cases-and-examples/deploying/deploying-to-azure-static-web-app)  
+
+**免责声明**：  
+本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而引起的任何误解或误读，我们概不负责。

translations/zh-CN/examples/03-image-classifier.ipynb

@@ -0,0 +1,397 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 简单图像分类器\n",
+    "\n",
+    "本笔记本将向您展示如何使用预训练的神经网络对图像进行分类。\n",
+    "\n",
+    "**您将学习：**\n",
+    "- 如何加载和使用预训练模型\n",
+    "- 图像预处理\n",
+    "- 对图像进行预测\n",
+    "- 理解置信度分数\n",
+    "\n",
+    "**使用场景：** 识别图像中的物体（例如“猫”、“狗”、“汽车”等）\n",
+    "\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 第一步：导入所需的库\n",
+    "\n",
+    "让我们导入所需的工具。如果你还不完全理解这些也不用担心！\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Core libraries\n",
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "import requests\n",
+    "from io import BytesIO\n",
+    "\n",
+    "# TensorFlow for deep learning\n",
+    "try:\n",
+    "    import tensorflow as tf\n",
+    "    from tensorflow.keras.applications import MobileNetV2\n",
+    "    from tensorflow.keras.applications.mobilenet_v2 import preprocess_input, decode_predictions\n",
+    "    print(\"✅ TensorFlow loaded successfully!\")\n",
+    "    print(f\"   Version: {tf.__version__}\")\n",
+    "except ImportError:\n",
+    "    print(\"❌ Please install TensorFlow: pip install tensorflow\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 第2步：加载预训练模型\n",
+    "\n",
+    "我们将使用 **MobileNetV2**，这是一个已经在数百万张图片上训练过的神经网络。\n",
+    "\n",
+    "这被称为 **迁移学习** - 使用别人训练好的模型！\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"📦 Loading pre-trained MobileNetV2 model...\")\n",
+    "print(\"   This may take a minute on first run (downloading weights)...\")\n",
+    "\n",
+    "# Load the model\n",
+    "# include_top=True means we use the classification layer\n",
+    "# weights='imagenet' means it was trained on ImageNet dataset\n",
+    "model = MobileNetV2(weights='imagenet', include_top=True)\n",
+    "\n",
+    "print(\"✅ Model loaded!\")\n",
+    "print(f\"   The model can recognize 1000 different object categories\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 第 3 步：辅助函数\n",
+    "\n",
+    "让我们创建一些函数，用于加载和准备模型所需的图像。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_image_from_url(url):\n",
+    "    \"\"\"\n",
+    "    Load an image from a URL.\n",
+    "    \n",
+    "    Args:\n",
+    "        url: Web address of the image\n",
+    "        \n",
+    "    Returns:\n",
+    "        PIL Image object\n",
+    "    \"\"\"\n",
+    "    response = requests.get(url)\n",
+    "    img = Image.open(BytesIO(response.content))\n",
+    "    return img\n",
+    "\n",
+    "\n",
+    "def prepare_image(img):\n",
+    "    \"\"\"\n",
+    "    Prepare an image for the model.\n",
+    "    \n",
+    "    Steps:\n",
+    "    1. Resize to 224x224 (model's expected size)\n",
+    "    2. Convert to array\n",
+    "    3. Add batch dimension\n",
+    "    4. Preprocess for MobileNetV2\n",
+    "    \n",
+    "    Args:\n",
+    "        img: PIL Image\n",
+    "        \n",
+    "    Returns:\n",
+    "        Preprocessed image array\n",
+    "    \"\"\"\n",
+    "    # Resize to 224x224 pixels\n",
+    "    img = img.resize((224, 224))\n",
+    "    \n",
+    "    # Convert to numpy array\n",
+    "    img_array = np.array(img)\n",
+    "    \n",
+    "    # Add batch dimension (model expects multiple images)\n",
+    "    img_array = np.expand_dims(img_array, axis=0)\n",
+    "    \n",
+    "    # Preprocess for MobileNetV2\n",
+    "    img_array = preprocess_input(img_array)\n",
+    "    \n",
+    "    return img_array\n",
+    "\n",
+    "\n",
+    "def classify_image(img):\n",
+    "    \"\"\"\n",
+    "    Classify an image and return top predictions.\n",
+    "    \n",
+    "    Args:\n",
+    "        img: PIL Image\n",
+    "        \n",
+    "    Returns:\n",
+    "        List of (class_name, confidence) tuples\n",
+    "    \"\"\"\n",
+    "    # Prepare the image\n",
+    "    img_array = prepare_image(img)\n",
+    "    \n",
+    "    # Make prediction\n",
+    "    predictions = model.predict(img_array, verbose=0)\n",
+    "    \n",
+    "    # Decode predictions to human-readable labels\n",
+    "    # top=5 means we get the top 5 most likely classes\n",
+    "    decoded = decode_predictions(predictions, top=5)[0]\n",
+    "    \n",
+    "    # Convert to simpler format\n",
+    "    results = [(label, float(confidence)) for (_, label, confidence) in decoded]\n",
+    "    \n",
+    "    return results\n",
+    "\n",
+    "\n",
+    "print(\"✅ Helper functions ready!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 第四步：在样本图像上进行测试\n",
+    "\n",
+    "让我们尝试对一些来自互联网的图像进行分类吧！\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Sample images to classify\n",
+    "# These are from Unsplash (free stock photos)\n",
+    "test_images = [\n",
+    "    {\n",
+    "        \"url\": \"https://images.unsplash.com/photo-1514888286974-6c03e2ca1dba?w=400\",\n",
+    "        \"description\": \"A cat\"\n",
+    "    },\n",
+    "    {\n",
+    "        \"url\": \"https://images.unsplash.com/photo-1552053831-71594a27632d?w=400\",\n",
+    "        \"description\": \"A dog\"\n",
+    "    },\n",
+    "    {\n",
+    "        \"url\": \"https://images.unsplash.com/photo-1511919884226-fd3cad34687c?w=400\",\n",
+    "        \"description\": \"A car\"\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "print(f\"🧪 Testing on {len(test_images)} images...\")\n",
+    "print(\"=\" * 70)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 分类每张图片\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for i, img_data in enumerate(test_images, 1):\n",
+    "    print(f\"\\n📸 Image {i}: {img_data['description']}\")\n",
+    "    print(\"-\" * 70)\n",
+    "    \n",
+    "    try:\n",
+    "        # Load image\n",
+    "        img = load_image_from_url(img_data['url'])\n",
+    "        \n",
+    "        # Display image\n",
+    "        display(img.resize((200, 200)))  # Show smaller version\n",
+    "        \n",
+    "        # Classify\n",
+    "        results = classify_image(img)\n",
+    "        \n",
+    "        # Show predictions\n",
+    "        print(\"\\n🎯 Top 5 Predictions:\")\n",
+    "        for rank, (label, confidence) in enumerate(results, 1):\n",
+    "            # Create a visual bar\n",
+    "            bar_length = int(confidence * 50)\n",
+    "            bar = \"█\" * bar_length\n",
+    "            \n",
+    "            print(f\"  {rank}. {label:20s} {confidence*100:5.2f}% {bar}\")\n",
+    "        \n",
+    "    except Exception as e:\n",
+    "        print(f\"❌ Error: {e}\")\n",
+    "\n",
+    "print(\"\\n\" + \"=\" * 70)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 第五步：尝试使用自己的图片！\n",
+    "\n",
+    "将下面的 URL 替换为您想要分类的任何图片 URL。\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Try your own image!\n",
+    "# Replace this URL with any image URL\n",
+    "custom_image_url = \"https://images.unsplash.com/photo-1472491235688-bdc81a63246e?w=400\"  # A flower\n",
+    "\n",
+    "print(\"🖼️  Classifying your custom image...\")\n",
+    "print(\"=\" * 70)\n",
+    "\n",
+    "try:\n",
+    "    # Load and show image\n",
+    "    img = load_image_from_url(custom_image_url)\n",
+    "    display(img.resize((300, 300)))\n",
+    "    \n",
+    "    # Classify\n",
+    "    results = classify_image(img)\n",
+    "    \n",
+    "    # Show results\n",
+    "    print(\"\\n🎯 Top 5 Predictions:\")\n",
+    "    print(\"-\" * 70)\n",
+    "    for rank, (label, confidence) in enumerate(results, 1):\n",
+    "        bar_length = int(confidence * 50)\n",
+    "        bar = \"█\" * bar_length\n",
+    "        print(f\"  {rank}. {label:20s} {confidence*100:5.2f}% {bar}\")\n",
+    "    \n",
+    "    # Highlight top prediction\n",
+    "    top_label, top_confidence = results[0]\n",
+    "    print(\"\\n\" + \"=\" * 70)\n",
+    "    print(f\"\\n🏆 Best guess: {top_label} ({top_confidence*100:.2f}% confident)\")\n",
+    "    \n",
+    "except Exception as e:\n",
+    "    print(f\"❌ Error: {e}\")\n",
+    "    print(\"   Make sure the URL points to a valid image!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 💡 刚刚发生了什么？\n",
+    "\n",
+    "1. **我们加载了一个预训练模型** - MobileNetV2 已经在数百万张图片上完成了训练  \n",
+    "2. **我们对图片进行了预处理** - 调整大小并格式化以适配模型  \n",
+    "3. **模型进行了预测** - 输出了1000个物体类别的概率  \n",
+    "4. **我们解码了结果** - 将数字转换为人类可读的标签  \n",
+    "\n",
+    "### 理解置信度分数\n",
+    "\n",
+    "- **90-100%**：非常有信心（几乎肯定正确）  \n",
+    "- **70-90%**：有信心（可能正确）  \n",
+    "- **50-70%**：信心一般（可能正确）  \n",
+    "- **低于50%**：信心不足（不确定）  \n",
+    "\n",
+    "### 为什么预测可能会出错？\n",
+    "\n",
+    "- **不寻常的角度或光线** - 模型是在典型照片上训练的  \n",
+    "- **多个物体** - 模型预期只有一个主要物体  \n",
+    "- **罕见物体** - 模型只识别1000个类别  \n",
+    "- **低质量图片** - 模糊或像素化的图片更难识别  \n",
+    "\n",
+    "---\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🚀 下一步\n",
+    "\n",
+    "1. **尝试不同的图片：**\n",
+    "   - 在 [Unsplash](https://unsplash.com) 上寻找图片\n",
+    "   - 右键点击 → “复制图片地址” 获取 URL\n",
+    "\n",
+    "2. **进行实验：**\n",
+    "   - 抽象艺术会有什么效果？\n",
+    "   - 它能识别不同角度的物体吗？\n",
+    "   - 它如何处理多个物体？\n",
+    "\n",
+    "3. **深入学习：**\n",
+    "   - 探索 [计算机视觉课程](../lessons/4-ComputerVision/README.md)\n",
+    "   - 学习如何训练自己的图像分类器\n",
+    "   - 理解 CNN（卷积神经网络）的工作原理\n",
+    "\n",
+    "---\n",
+    "\n",
+    "## 🎉 恭喜！\n",
+    "\n",
+    "你刚刚使用最先进的神经网络构建了一个图像分类器！\n",
+    "\n",
+    "这种技术同样驱动了：\n",
+    "- Google Photos（整理你的照片）\n",
+    "- 自动驾驶汽车（识别物体）\n",
+    "- 医学诊断（分析 X 光片）\n",
+    "- 质量控制（检测缺陷）\n",
+    "\n",
+    "继续探索和学习吧！🚀\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.0"
+  },
+  "coopTranslator": {
+   "original_hash": "1d472141d9df46b751542b3c29f88677",
+   "translation_date": "2025-10-03T11:39:30+00:00",
+   "source_file": "examples/03-image-classifier.ipynb",
+   "language_code": "zh"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

translations/zh-CN/examples/README.md

@@ -0,0 +1,85 @@
+# 初学者友好的 AI 示例
+
+欢迎！本目录包含简单、独立的示例，旨在帮助您入门 AI 和机器学习。每个示例都设计为对初学者友好，并附有详细的注释和逐步的解释。
+
+## 📚 示例概览
+
+| 示例 | 描述 | 难度 | 前置知识 |
+|------|------|------|----------|
+| [Hello AI World](../../../examples/01-hello-ai-world.py) | 您的第一个 AI 程序——简单的模式识别 | ⭐ 初学者 | Python 基础 |
+| [Simple Neural Network](../../../examples/02-simple-neural-network.py) | 从零开始构建一个神经网络 | ⭐⭐ 初学者+ | Python，基础数学 |
+| [Image Classifier](./03-image-classifier.ipynb) | 使用预训练模型对图像进行分类 | ⭐⭐ 初学者+ | Python，numpy |
+| [Text Sentiment](../../../examples/04-text-sentiment.py) | 分析文本情感（正面/负面） | ⭐⭐ 初学者+ | Python |
+
+## 🚀 入门指南
+
+### 前置知识
+
+确保您已安装 Python（推荐 3.8 或更高版本）。安装所需的包：
+
+```bash
+# For Python scripts
+pip install numpy
+
+# For Jupyter notebooks (image classifier)
+pip install jupyter numpy pillow tensorflow
+```
+
+或者使用主课程中的 conda 环境：
+
+```bash
+conda env create --name ai4beg --file ../environment.yml
+conda activate ai4beg
+```
+
+### 运行示例
+
+**对于 Python 脚本 (.py 文件)：**
+```bash
+python 01-hello-ai-world.py
+```
+
+**对于 Jupyter 笔记本 (.ipynb 文件)：**
+```bash
+jupyter notebook 03-image-classifier.ipynb
+```
+
+## 📖 学习路径
+
+我们建议按顺序学习以下示例：
+
+1. **从“Hello AI World”开始** - 学习模式识别的基础知识
+2. **构建一个简单的神经网络** - 理解神经网络的工作原理
+3. **尝试图像分类器** - 使用真实图像体验 AI 的实际应用
+4. **分析文本情感** - 探索自然语言处理
+
+## 💡 初学者提示
+
+- **仔细阅读代码注释** - 它们解释了每一行代码的作用
+- **大胆尝试！** - 尝试修改参数，观察结果
+- **不要担心无法完全理解** - 学习是一个循序渐进的过程
+- **提出问题** - 使用 [讨论板](https://github.com/microsoft/AI-For-Beginners/discussions)
+
+## 🔗 下一步
+
+完成这些示例后，可以探索完整课程：
+- [AI 简介](../lessons/1-Intro/README.md)
+- [神经网络](../lessons/3-NeuralNetworks/README.md)
+- [计算机视觉](../lessons/4-ComputerVision/README.md)
+- [自然语言处理](../lessons/5-NLP/README.md)
+
+## 🤝 贡献
+
+觉得这些示例有帮助吗？欢迎帮助我们改进：
+- 报告问题或提出改进建议
+- 添加更多适合初学者的示例
+- 改进文档和注释
+
+---
+
+*记住：每个专家都曾是初学者。祝学习愉快！ 🎓*
+
+---
+
+**免责声明**：  
+本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而引起的任何误解或误读，我们概不负责。

translations/zh-CN/lessons/0-course-setup/for-teachers.md

@@ -0,0 +1,26 @@
+# 给教育工作者
+
+想在课堂上使用这套课程吗？请随意使用！
+
+事实上，您可以通过 GitHub Classroom 在 GitHub 上直接使用它。
+
+要做到这一点，请先 fork 此仓库。您需要为每节课创建一个单独的仓库，因此需要将每个文件夹提取到一个独立的仓库中。这样，[GitHub Classroom](https://classroom.github.com/classrooms) 就可以分别识别每节课。
+
+这些[完整的说明](https://github.blog/2020-03-18-set-up-your-digital-classroom-with-github-classroom/)可以帮助您了解如何设置您的课堂。
+
+## 按原样使用此仓库
+
+如果您希望按当前形式使用此仓库，而不使用 GitHub Classroom，这也是可以的。您需要与学生沟通，一起完成哪一节课程。
+
+在在线教学环境中（如 Zoom、Teams 或其他平台），您可以为测验创建分组讨论室，并指导学生为学习做好准备。然后邀请学生参加测验，并在特定时间以“issues”的形式提交答案。如果您希望学生公开协作完成作业，也可以采用类似的方式。
+
+如果您更喜欢更私密的形式，可以让学生逐节 fork 课程到他们自己的 GitHub 私有仓库，并授予您访问权限。这样，他们可以私下完成测验和作业，并通过您课堂仓库中的 issues 提交给您。
+
+在在线课堂中，有很多方法可以让这套课程发挥作用。请告诉我们哪种方式最适合您！
+
+## 请分享您的想法
+
+我们希望这套课程能够满足您和学生的需求。请在讨论区中给我们反馈！
+
+**免责声明**：  
+本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。我们对于因使用此翻译而引起的任何误解或误读不承担责任。