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code

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4.1前言.md

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4.2机器学习（AI）快速入门（quick start）.md

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4.3.1.1程序示例——maze迷宫解搜索.md

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4.3.1.2项目：Tic-Tac-Toe井字棋.md

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4.3.1搜索.md

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4.3.2.1程序示例——命题逻辑与模型检测.md

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4.3.2.2项目：扫雷，骑士与流氓问题.md

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4.3.2知识推理.md

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4.3.3.1程序示例.md

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4.3.3.2项目：遗传.md

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4.3.3不确定性问题.md

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4.3人工智能导论及机器学习入门.md

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4.4FAQ：常见问题.md

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4.6.1工欲善其事，必先利其器.md

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4.6.2你可能会需要的术语介绍.md

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4.6.3深度学习快速入门.md

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4.6.4Pytorch安装.md

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4.6.5.1CV领域任务(研究目标).md

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4.6.5.2.1数据读取.md

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4.6.5.2.2数据增强.md

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4.6.5.2CV中的数据预处理（torchvision）.md

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4.6.5.3.1AlexNet.md

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4.6.5.3.2FCN.md

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4.6.5.3.3ResNet.md

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4.6.5.3.4UNet.md

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4.6.5.3.5GAN.md

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4.6.5.3.6思考题参考.md

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4.6.5.3.7还要学更多？.md

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4.6.5.3CV中的经典网络.md

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4.6.5.4.1NeRF.md

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4.6.5.4.2NeRF的改进方向.md

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4.6.5.4.3自制数据集的工具COLMAP.md

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4.6.5.4神经辐射场(NeRF).md

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4.6.5.5行人重识别(ReID).md

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4.6.5计算机视觉（CV）.md

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4.6.6.1NLP领域任务（研究目标）.md

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4.6.6.2.1推荐系统经典模型综述.md

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4.6.6.2.2.1《推荐系统实践》读后的一些想法.md

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4.6.6.2.2.2推荐系统概念解释 and 一个好的推荐系统.md

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4.6.6.2.2.3推荐系统实例.md

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4.6.6.2.2.4利用用户行为数据.md

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4.6.6.2.2.5推荐系统冷启动.md

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4.6.6.2.2.6利用标签信息.md

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4.6.6.2.2.7利用上下文信息.md

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4.6.6.2.2基于数据的角度，看待推荐系统的构造.md

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4.6.6.2.3序列化推荐.md

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4.6.6.2推荐系统.md

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4.6.6.3知识图谱.md

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4.6.6自然语言处理（NLP）.md

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4.6.7.1VIT.md

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4.6.7.2BERT.md

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4.6.7.3MAE.md

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4.6.7Transformer.md

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4.6.8.1前言.md

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4.6.8.2Inst Disc.md

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4.6.8.3定义正负样本的方式.md

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4.6.8.4MoCo.md

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4.6.8.5SimCLR.md

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4.6.8.6SwAV.md

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4.6.8.7BYOL.md

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4.6.8.8SimSiam.md

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4.6.8.9MoCo v3.md

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4.6.8.10总结.md

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4.6.8对比学习.md

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4.6.9.1前言.md

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4.6.9.2基础资料推荐.md

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4.6.9.3基本概念介绍.md

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4.6.9深度强化学习.md

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4.6深度学习.md

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4.7图网络略述（intro&GCN）.md

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4.8数据分析.md

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4.9如何做研究.md

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4.10科研论文写作.md

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4.11从 AI 到 智能系统 —— 从 LLMs 到 Agents.md

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4.12本章节内容的局限性.md

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4.人工智能.md

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FunRec概述.md

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SRT.md

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