refactor: 重组项目目录结构

以讲义内容为骨架迁移到标准目录格式：
- officefile/ 主内容（12章 + 附录 + CC4SI补充）
- dofile/ 代码示例（11个Python脚本）
- data/ 图片资源
- output/ 生成输出（忽略）
- Archive/ 归档旧目录（忽略）
- .claude/skills/ 保留markdown-to-docx工具链
- .pandoc/ 保留CSL和本地化配置

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

officefile/appendix/references.md

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+# 参考文献
+
+本部分整理教材中引用的论文、书籍和在线资源。
+
+---
+
+## 论文
+
+### 基础理论
+
+- Universal Approximation Theorem (1989)
+- Scaling Laws for Neural Language Models (2020)
+
+### 计算机视觉
+
+- AlexNet (2012) - ImageNet Classification with Deep Convolutional Neural Networks
+- ResNet (2015) - Deep Residual Learning for Image Recognition
+- YOLO (2016) - You Only Look Once: Unified, Real-Time Object Detection
+- U-Net (2015) - Convolutional Networks for Biomedical Image Segmentation
+
+### Transformer与大模型
+
+- Attention Is All You Need (2017)
+- BERT: Pre-training of Deep Bidirectional Transformers (2018)
+- GPT-3: Language Models are Few-Shot Learners (2020)
+- Training Language Models to Follow Instructions with Human Feedback (2022)
+
+### 生成式AI
+
+- Denoising Diffusion Probabilistic Models (2020)
+- High-Resolution Image Synthesis with Latent Diffusion Models (2022)
+- ControlNet (2023)
+
+### AI Agent
+
+- LLM Powered Autonomous Agents (2023)
+- ReAct: Synergizing Reasoning and Acting in Language Models (2022)
+
+---
+
+## 书籍
+
+### 深度学习
+
+- Deep Learning (Ian Goodfellow et al.)
+- Neural Networks and Deep Learning (Michael Nielsen)
+
+### 强化学习
+
+- Reinforcement Learning: An Introduction (Sutton & Barto)
+
+---
+
+## 在线资源
+
+### 课程
+
+- CS231n: Convolutional Neural Networks for Visual Recognition
+- Fast.ai Practical Deep Learning for Coders
+
+### 工具文档
+
+- PyTorch: https://pytorch.org/docs/
+- Ultralytics YOLO: https://docs.ultralytics.com/
+- LangChain: https://python.langchain.com/
+
+### 博客
+
+- Lil'Log (Lilian Weng): https://lilianweng.github.io/
+- The Illustrated Transformer: https://jalammar.github.io/illustrated-transformer/
+
+---
+
+## 设计AI相关
+
+### 学术期刊
+
+- Landscape and Urban Planning
+- Environment and Planning B: Urban Analytics and City Science
+- Automation in Construction
+
+### 会议
+
+- CAAD Futures
+- ACADIA
+- eCAADe
+
+---
+
+## 数据集
+
+### 计算机视觉
+
+- ImageNet
+- COCO (Common Objects in Context)
+- MNIST
+
+### 空间数据
+
+- OpenStreetMap
+- 路网数据、POI数据等
+
+---
+
+## 许可说明
+
+部分内容引用自公开资源，遵循相应许可协议使用。
+
+---
+
+**最后更新**：2026年4月