pengxiao
219232de74
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>
112 lines
2.2 KiB
Markdown
112 lines
2.2 KiB
Markdown
# 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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- 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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- 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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- 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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- 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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- LLM Powered Autonomous Agents (2023)
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- ReAct: Synergizing Reasoning and Acting in Language Models (2022)
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---
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## 书籍
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### 深度学习
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- Deep Learning (Ian Goodfellow et al.)
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- Neural Networks and Deep Learning (Michael Nielsen)
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### 强化学习
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- Reinforcement Learning: An Introduction (Sutton & Barto)
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---
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## 在线资源
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### 课程
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- CS231n: Convolutional Neural Networks for Visual Recognition
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- Fast.ai Practical Deep Learning for Coders
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### 工具文档
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- PyTorch: https://pytorch.org/docs/
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- Ultralytics YOLO: https://docs.ultralytics.com/
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- LangChain: https://python.langchain.com/
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### 博客
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- Lil'Log (Lilian Weng): https://lilianweng.github.io/
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- The Illustrated Transformer: https://jalammar.github.io/illustrated-transformer/
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---
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## 设计AI相关
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### 学术期刊
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- Landscape and Urban Planning
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- Environment and Planning B: Urban Analytics and City Science
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- Automation in Construction
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### 会议
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- CAAD Futures
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- ACADIA
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- eCAADe
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---
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## 数据集
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### 计算机视觉
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- ImageNet
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- COCO (Common Objects in Context)
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- MNIST
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### 空间数据
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- OpenStreetMap
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- 路网数据、POI数据等
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---
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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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---
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**最后更新**：2026年4月
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