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# 附录7：参考文献
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本部分整理教材中引用的论文、书籍和在线资源。
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论文
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# 基础理论
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4. Universal Approximation Theorem (1989)
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Scaling Laws for Neural Language Models (2020)
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# 计算机视觉
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6. AlexNet (2012) - ImageNet Classification with Deep Convolutional Neural Networks
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ResNet (2015) - Deep Residual Learning for Image Recognition
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YOLO (2016) - You Only Look Once: Unified, Real-Time Object Detection
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U-Net (2015) - Convolutional Networks for Biomedical Image Segmentation
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# Transformer与大模型
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10. Attention Is All You Need (2017)
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BERT: Pre-training of Deep Bidirectional Transformers (2018)
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GPT-3: Language Models are Few-Shot Learners (2020)
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Training Language Models to Follow Instructions with Human Feedback (2022)
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# 生成式AI
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14. Denoising Diffusion Probabilistic Models (2020)
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High-Resolution Image Synthesis with Latent Diffusion Models (2022)
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ControlNet (2023)
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# AI Agent
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1. LLM Powered Autonomous Agents (2023)
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2. ReAct: Synergizing Reasoning and Acting in Language Models (2022)
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3.
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# 书籍
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5. 深度学习
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6. Deep Learning (Ian Goodfellow et al.)
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7. Neural Networks and Deep Learning (Michael Nielsen)
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8. 强化学习
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9. Reinforcement Learning: An Introduction (Sutton & Barto)
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10.
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# 在线资源
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12. 课程
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13. CS231n: Convolutional Neural Networks for Visual Recognition
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14. Fast.ai Practical Deep Learning for Coders
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## 工具文档
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16. PyTorch: https://pytorch.org/docs/
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17. Ultralytics YOLO: https://docs.ultralytics.com/
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18. LangChain: https://python.langchain.com/
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## 博客
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20. Lil'Log (Lilian Weng): https://lilianweng.github.io/
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21. The Illustrated Transformer: https://jalammar.github.io/illustrated-transformer/
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22.
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# 设计AI相关
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## 学术期刊
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25. Landscape and Urban Planning
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26. Environment and Planning B: Urban Analytics and City Science
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27. Automation in Construction
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## 会议
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29. CAAD Futures
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30. ACADIA
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31. eCAADe
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32.
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# 数据集
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## 计算机视觉
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35. ImageNet
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36. COCO (Common Objects in Context)
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37. MNIST
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## 空间数据
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39. OpenStreetMap
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40. 路网数据、POI数据等
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# 许可说明
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部分内容引用自公开资源，遵循相应许可协议使用。
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**最后更新**：2026年4月
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