{"courses":[{"id":"lesson-001","number":1,"title":"人工智能的定义与发展简史","moduleFolder":"A1_AI基础认知","moduleCode":"A1","moduleTitle":"AI基础认知","level":"五级/初级","color":"#00D9FF","hours":2,"relativePath":"A1_AI基础认知/第01课_人工智能的定义与发展简史.md","pptxPath":"pptx/A1_AI基础认知/第01课_人工智能的定义与发展简史.pptx","headings":["人工智能的定义","从“机器能思考吗”到大模型与智能体","为什么先学 AI 发展史？","学习目标","什么是人工智能？","AI 的四类核心能力","图灵测试：问题的起点","AI 发展的三次浪潮","第一次浪潮：符号主义（1956—1974）","第二次浪潮：专家系统（1980—1987）","第三次浪潮：深度学习与大模型（2006—至今）","关键里程碑时间线","AlphaGo 为什么是里程碑？","ChatGPT 为什么是转折点？","大模型的“涌现能力”","AI 应用领域全景","AI 训练师在产业链中的位置","AI 训练师的核心价值","课堂练习：AI 发展史知识卡片","本节课总结与作业"],"summary":"人工智能的定义 与发展简史 从“机器能思考吗”到大模型与智能体 AI 基础认知 · 第 01 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 看懂浪潮 · 连接岗位 为什么先学 AI 发展史？…"},{"id":"lesson-002","number":2,"title":"AI三要素：数据、算法与算力","moduleFolder":"A1_AI基础认知","moduleCode":"A1","moduleTitle":"AI基础认知","level":"五级/初级","color":"#00D9FF","hours":2,"relativePath":"A1_AI基础认知/第02课_AI三要素：数据、算法与算力.md","pptxPath":"pptx/A1_AI基础认知/第02课_AI三要素：数据、算法与算力.pptx","headings":["第 02 课  AI三要素：数据、算法与算力","A1_AI基础认知 · 五级/初级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：从通识到岗位：大学生为什么要懂AI训练","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 02 课 AI三要素：数据、算法与算力 A1AI基础认知 · 五级/初级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 AI三要素：数据、算法与算力，解决真实项目中的三个问题： 1. 概念边…"},{"id":"lesson-003","number":3,"title":"机器学习 vs 深度学习 vs 大模型","moduleFolder":"A1_AI基础认知","moduleCode":"A1","moduleTitle":"AI基础认知","level":"五级/初级","color":"#00D9FF","hours":2,"relativePath":"A1_AI基础认知/第03课_机器学习_vs_深度学习_vs_大模型.md","pptxPath":"pptx/A1_AI基础认知/第03课_机器学习_vs_深度学习_vs_大模型.pptx","headings":["机器学习 vs 深度学习","从“学习规律”到“通用智能接口”","为什么要区分这三个概念？","学习目标","三者的层次关系","机器学习：最广泛的方法集合","传统机器学习适合什么？","深度学习：让模型自动学习特征","传统 ML vs 深度学习","主要深度学习架构","大模型：深度学习的大规模预训练产物","大模型的涌现能力","代表性大模型类型","三者的数据需求对比","实际工作流程对比","如何根据任务选择方法？","智慧图书馆场景：三类技术怎么用？","课堂练习：判断模型类型","常见误区","本节课总结与作业"],"summary":"机器学习 vs 深度学习 vs 大模型 从“学习规律”到“通用智能接口” AI 基础认知 · 第 03 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 分清层级 · 选择方法 · 连接岗位 为什么要区分这三个概念？…"},{"id":"lesson-004","number":4,"title":"AI伦理、数据安全与职业边界","moduleFolder":"A1_AI基础认知","moduleCode":"A1","moduleTitle":"AI基础认知","level":"五级/初级","color":"#00D9FF","hours":2,"relativePath":"A1_AI基础认知/第04课_AI伦理、数据安全与职业边界.md","pptxPath":"pptx/A1_AI基础认知/第04课_AI伦理、数据安全与职业边界.pptx","headings":["第 04 课  AI伦理、数据安全与职业边界","A1_AI基础认知 · 五级/初级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：从通识到岗位：大学生为什么要懂AI训练","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 04 课 AI伦理、数据安全与职业边界 A1AI基础认知 · 五级/初级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 AI伦理、数据安全与职业边界，解决真实项目中的三个问题： 1. 概念边…"},{"id":"lesson-005","number":5,"title":"AI 训练师职业认知与等级体系","moduleFolder":"A1_AI基础认知","moduleCode":"A1","moduleTitle":"AI基础认知","level":"五级/初级","color":"#00D9FF","hours":2,"relativePath":"A1_AI基础认知/第05课_AI_训练师职业认知与等级体系.md","pptxPath":"pptx/A1_AI基础认知/第05课_AI_训练师职业认知与等级体系.pptx","headings":["AI 训练师职业认知与等级体系","从核心概念到训练师实战","为什么学习《AI 训练师职业认知与等级体系》？","学习目标","5.1 人工智能训练师是什么？","5.2 五级等级体系","5.3 五级（初级工）职责","5.4 四级（中级工）职责","5.5 三级（高级工）职责","5.6 职业发展路径","5.7 薪资水平参考","5.8 AI 训练师 vs 相关岗位","5.9 考证与就业","5.10 课堂练习：等级匹配","5.11 课后作业","动手实践","案例讨论","随堂测验","常见错误","本节课总结"],"summary":"AI 训练师职业认知与等级体系 从核心概念到训练师实战 AI基础认知 · 第 05 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《AI 训练师职业认知与等级体系》？…"},{"id":"lesson-006","number":6,"title":"标注行业规范与质量控制标准","moduleFolder":"A1_AI基础认知","moduleCode":"A1","moduleTitle":"AI基础认知","level":"五级/初级","color":"#00D9FF","hours":2,"relativePath":"A1_AI基础认知/第06课_标注行业规范与质量控制标准.md","pptxPath":"pptx/A1_AI基础认知/第06课_标注行业规范与质量控制标准.pptx","headings":["标注行业规范与质量控制标准","从核心概念到训练师实战","为什么学习《标注行业规范与质量控制标准》？","学习目标","6.1 数据标注行业的标准化进程","6.2 标注项目的生命周期","6.3 标注规范文档（SOP）的结构","6.4 质量指标体系","6.5 Kappa 系数简介","6.6 标注质量控制流程","6.7 标注员自检 Checklist","6.8 常见标注错误类型","6.9 课堂练习：质检实操","6.10 课后作业","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业"],"summary":"标注行业规范与质量控制标准 从核心概念到训练师实战 AI基础认知 · 第 06 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《标注行业规范与质量控制标准》？…"},{"id":"lesson-007","number":7,"title":"计算机操作基础","moduleFolder":"A1_AI基础认知","moduleCode":"A1","moduleTitle":"AI基础认知","level":"五级/初级","color":"#00D9FF","hours":2,"relativePath":"A1_AI基础认知/第07课_计算机操作基础.md","pptxPath":"pptx/A1_AI基础认知/第07课_计算机操作基础.pptx","headings":["计算机操作基础","从核心概念到训练师实战","为什么学习《计算机操作基础》？","学习目标","7.1 文件系统基础","7.2 文件目录结构规范","7.3 标注工具概览","7.4 LabelStudio 基础操作","7.5 键盘快捷键提升效率","7.6 数据格式基础：JSON","7.7 数据格式基础：CSV","7.8 数据格式基础：XML","7.9 格式转换实操","JSON 转 CSV","7.10 课堂练习：文件操作实战","7.11 课后作业","动手实践","案例讨论","随堂测验","常见错误"],"summary":"计算机操作基础 从核心概念到训练师实战 AI基础认知 · 第 07 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《计算机操作基础》？…"},{"id":"lesson-008","number":8,"title":"数据类型：结构化、非结构化与半结构化","moduleFolder":"A2_数据采集与整理","moduleCode":"A2","moduleTitle":"数据采集与整理","level":"五级/初级","color":"#2ECC71","hours":2,"relativePath":"A2_数据采集与整理/第08课_数据类型：结构化、非结构化与半结构化.md","pptxPath":"pptx/A2_数据采集与整理/第08课_数据类型：结构化、非结构化与半结构化.pptx","headings":["第 08 课  数据类型：结构化、非结构化与半结构化","A2_数据采集与整理 · 五级/初级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：大学生数据素养：从采集到可训练数据集","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 08 课 数据类型：结构化、非结构化与半结构化 A2数据采集与整理 · 五级/初级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 数据类型：结构化、非结构化与半结构化，解决真实项目中的三个问…"},{"id":"lesson-009","number":9,"title":"文本数据采集：网页、PDF与OCR","moduleFolder":"A2_数据采集与整理","moduleCode":"A2","moduleTitle":"数据采集与整理","level":"五级/初级","color":"#2ECC71","hours":2,"relativePath":"A2_数据采集与整理/第09课_文本数据采集：网页、PDF与OCR.md","pptxPath":"pptx/A2_数据采集与整理/第09课_文本数据采集：网页、PDF与OCR.pptx","headings":["第 09 课  文本数据采集：网页、PDF与OCR","A2_数据采集与整理 · 五级/初级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：大学生数据素养：从采集到可训练数据集","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 09 课 文本数据采集：网页、PDF与OCR A2数据采集与整理 · 五级/初级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 文本数据采集：网页、PDF与OCR，解决真实项目中的三个问题：…"},{"id":"lesson-010","number":10,"title":"图像数据采集与公开数据集","moduleFolder":"A2_数据采集与整理","moduleCode":"A2","moduleTitle":"数据采集与整理","level":"五级/初级","color":"#2ECC71","hours":2,"relativePath":"A2_数据采集与整理/第10课_图像数据采集与公开数据集.md","pptxPath":"pptx/A2_数据采集与整理/第10课_图像数据采集与公开数据集.pptx","headings":["第 10 课  图像数据采集与公开数据集","A2_数据采集与整理 · 五级/初级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：大学生数据素养：从采集到可训练数据集","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 10 课 图像数据采集与公开数据集 A2数据采集与整理 · 五级/初级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 图像数据采集与公开数据集，解决真实项目中的三个问题： 1. 概念边界不清…"},{"id":"lesson-011","number":11,"title":"语音数据采集与采样率","moduleFolder":"A2_数据采集与整理","moduleCode":"A2","moduleTitle":"数据采集与整理","level":"五级/初级","color":"#2ECC71","hours":2,"relativePath":"A2_数据采集与整理/第11课_语音数据采集与采样率.md","pptxPath":"pptx/A2_数据采集与整理/第11课_语音数据采集与采样率.pptx","headings":["第 11 课  语音数据采集与采样率","A2_数据采集与整理 · 五级/初级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：大学生数据素养：从采集到可训练数据集","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 11 课 语音数据采集与采样率 A2数据采集与整理 · 五级/初级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 语音数据采集与采样率，解决真实项目中的三个问题： 1. 概念边界不清； 2.…"},{"id":"lesson-012","number":12,"title":"数据采集合法合规","moduleFolder":"A2_数据采集与整理","moduleCode":"A2","moduleTitle":"数据采集与整理","level":"五级/初级","color":"#2ECC71","hours":2,"relativePath":"A2_数据采集与整理/第12课_数据采集合法合规.md","pptxPath":"pptx/A2_数据采集与整理/第12课_数据采集合法合规.pptx","headings":["第 12 课  数据采集合法合规","A2_数据采集与整理 · 五级/初级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：大学生数据素养：从采集到可训练数据集","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 12 课 数据采集合法合规 A2数据采集与整理 · 五级/初级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 数据采集合法合规，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流…"},{"id":"lesson-013","number":13,"title":"缺失值识别与处理","moduleFolder":"A2_数据采集与整理","moduleCode":"A2","moduleTitle":"数据采集与整理","level":"五级/初级","color":"#2ECC71","hours":2,"relativePath":"A2_数据采集与整理/第13课_缺失值识别与处理.md","pptxPath":"pptx/A2_数据采集与整理/第13课_缺失值识别与处理.pptx","headings":["第 13 课  缺失值识别与处理","A2_数据采集与整理 · 五级/初级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：大学生数据素养：从采集到可训练数据集","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 13 课 缺失值识别与处理 A2数据采集与整理 · 五级/初级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 缺失值识别与处理，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流…"},{"id":"lesson-014","number":14,"title":"重复数据检测与去重","moduleFolder":"A2_数据采集与整理","moduleCode":"A2","moduleTitle":"数据采集与整理","level":"五级/初级","color":"#2ECC71","hours":2,"relativePath":"A2_数据采集与整理/第14课_重复数据检测与去重.md","pptxPath":"pptx/A2_数据采集与整理/第14课_重复数据检测与去重.pptx","headings":["第 14 课  重复数据检测与去重","A2_数据采集与整理 · 五级/初级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：大学生数据素养：从采集到可训练数据集","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 14 课 重复数据检测与去重 A2数据采集与整理 · 五级/初级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 重复数据检测与去重，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操…"},{"id":"lesson-015","number":15,"title":"异常值识别与记录","moduleFolder":"A2_数据采集与整理","moduleCode":"A2","moduleTitle":"数据采集与整理","level":"五级/初级","color":"#2ECC71","hours":2,"relativePath":"A2_数据采集与整理/第15课_异常值识别与记录.md","pptxPath":"pptx/A2_数据采集与整理/第15课_异常值识别与记录.pptx","headings":["第 15 课  异常值识别与记录","A2_数据采集与整理 · 五级/初级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：大学生数据素养：从采集到可训练数据集","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 15 课 异常值识别与记录 A2数据采集与整理 · 五级/初级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 异常值识别与记录，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流…"},{"id":"lesson-016","number":16,"title":"数据格式统一：CSV、JSON、XML、TXT","moduleFolder":"A2_数据采集与整理","moduleCode":"A2","moduleTitle":"数据采集与整理","level":"五级/初级","color":"#2ECC71","hours":2,"relativePath":"A2_数据采集与整理/第16课_数据格式统一：CSV、JSON、XML、TXT.md","pptxPath":"pptx/A2_数据采集与整理/第16课_数据格式统一：CSV、JSON、XML、TXT.pptx","headings":["第 16 课  数据格式统一：CSV、JSON、XML、TXT","A2_数据采集与整理 · 五级/初级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：大学生数据素养：从采集到可训练数据集","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 16 课 数据格式统一：CSV、JSON、XML、TXT A2数据采集与整理 · 五级/初级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 数据格式统一：CSV、JSON、XML、TXT，解…"},{"id":"lesson-017","number":17,"title":"训练集、验证集、测试集划分","moduleFolder":"A2_数据采集与整理","moduleCode":"A2","moduleTitle":"数据采集与整理","level":"五级/初级","color":"#2ECC71","hours":2,"relativePath":"A2_数据采集与整理/第17课_训练集、验证集、测试集划分.md","pptxPath":"pptx/A2_数据采集与整理/第17课_训练集、验证集、测试集划分.pptx","headings":["第 17 课  训练集、验证集、测试集划分","A2_数据采集与整理 · 五级/初级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：大学生数据素养：从采集到可训练数据集","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 17 课 训练集、验证集、测试集划分 A2数据采集与整理 · 五级/初级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 训练集、验证集、测试集划分，解决真实项目中的三个问题： 1. 概念边界…"},{"id":"lesson-018","number":18,"title":"数据目录结构规范","moduleFolder":"A2_数据采集与整理","moduleCode":"A2","moduleTitle":"数据采集与整理","level":"五级/初级","color":"#2ECC71","hours":2,"relativePath":"A2_数据采集与整理/第18课_数据目录结构规范.md","pptxPath":"pptx/A2_数据采集与整理/第18课_数据目录结构规范.pptx","headings":["第 18 课  数据目录结构规范","A2_数据采集与整理 · 五级/初级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：大学生数据素养：从采集到可训练数据集","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 18 课 数据目录结构规范 A2数据采集与整理 · 五级/初级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 数据目录结构规范，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流…"},{"id":"lesson-019","number":19,"title":"数据版本管理基础","moduleFolder":"A2_数据采集与整理","moduleCode":"A2","moduleTitle":"数据采集与整理","level":"五级/初级","color":"#2ECC71","hours":2,"relativePath":"A2_数据采集与整理/第19课_数据版本管理基础.md","pptxPath":"pptx/A2_数据采集与整理/第19课_数据版本管理基础.pptx","headings":["第 19 课  数据版本管理基础","A2_数据采集与整理 · 五级/初级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：大学生数据素养：从采集到可训练数据集","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 19 课 数据版本管理基础 A2数据采集与整理 · 五级/初级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 数据版本管理基础，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流…"},{"id":"lesson-020","number":20,"title":"数据标注清单编制","moduleFolder":"A2_数据采集与整理","moduleCode":"A2","moduleTitle":"数据采集与整理","level":"五级/初级","color":"#2ECC71","hours":2,"relativePath":"A2_数据采集与整理/第20课_数据标注清单编制.md","pptxPath":"pptx/A2_数据采集与整理/第20课_数据标注清单编制.pptx","headings":["第 20 课  数据标注清单编制","A2_数据采集与整理 · 五级/初级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：大学生数据素养：从采集到可训练数据集","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 20 课 数据标注清单编制 A2数据采集与整理 · 五级/初级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 数据标注清单编制，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流…"},{"id":"lesson-021","number":21,"title":"文本分类标注","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第21课_文本分类标注.md","pptxPath":"pptx/A3_A4_标注与质检/第21课_文本分类标注.pptx","headings":["文本分类标注","从核心概念到训练师实战","为什么学习《文本分类标注》？","学习目标","21.1 什么是文本分类？","21.2 单标签 vs 多标签","21.3 文本分类标注规则示例","21.4 标注步骤实操","21.5 课堂练习：情感分类标注","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 15：任务单","考试实操 16：样本表设计","考试实操 17：小样本试跑","考试实操 18：操作步骤","考试实操 19：质量检查清单"],"summary":"文本分类标注 从核心概念到训练师实战 标注与质检 · 第 21 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《文本分类标注》？…"},{"id":"lesson-022","number":22,"title":"命名实体识别 NER 标注","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第22课_命名实体识别_NER_标注.md","pptxPath":"pptx/A3_A4_标注与质检/第22课_命名实体识别_NER_标注.pptx","headings":["命名实体识别（NER）标注","从核心概念到训练师实战","为什么学习《命名实体识别（NER）标注》？","学习目标","22.1 什么是 NER？","22.2 BIO 标注格式","22.3 NER 标注的常见困难","22.4 标注工具中的 NER 操作","22.5 课堂练习：NER 标注","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 15：任务单","考试实操 16：样本表设计","考试实操 17：小样本试跑","考试实操 18：操作步骤","考试实操 19：质量检查清单"],"summary":"命名实体识别（NER）标注 从核心概念到训练师实战 标注与质检 · 第 22 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《命名实体识别（NER）标注》？…"},{"id":"lesson-023","number":23,"title":"关系抽取标注","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第23课_关系抽取标注.md","pptxPath":"pptx/A3_A4_标注与质检/第23课_关系抽取标注.pptx","headings":["关系抽取标注","从核心概念到训练师实战","为什么学习《关系抽取标注》？","学习目标","23.1 什么是关系抽取？","23.2 常见关系类型","23.3 关系标注的操作方式","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 13：任务单","考试实操 14：样本表设计","考试实操 15：小样本试跑","考试实操 16：操作步骤","考试实操 17：质量检查清单","考试实操 18：错例分析","考试实操 19：智慧图书馆应用"],"summary":"关系抽取标注 从核心概念到训练师实战 标注与质检 · 第 23 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《关系抽取标注》？…"},{"id":"lesson-024","number":24,"title":"语义相似度标注","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第24课_语义相似度标注.md","pptxPath":"pptx/A3_A4_标注与质检/第24课_语义相似度标注.pptx","headings":["语义相似度标注","从核心概念到训练师实战","为什么学习《语义相似度标注》？","学习目标","24.1 什么是语义相似度？","24.2 相似度等级标注","24.3 标注实操","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 13：任务单","考试实操 14：样本表设计","考试实操 15：小样本试跑","考试实操 16：操作步骤","考试实操 17：质量检查清单","考试实操 18：错例分析","考试实操 19：智慧图书馆应用"],"summary":"语义相似度标注 从核心概念到训练师实战 标注与质检 · 第 24 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《语义相似度标注》？…"},{"id":"lesson-025","number":25,"title":"标注质量自检","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第25课_标注质量自检.md","pptxPath":"pptx/A3_A4_标注与质检/第25课_标注质量自检.pptx","headings":["标注质量自检","从核心概念到训练师实战","为什么学习《标注质量自检》？","学习目标","25.1 为什么需要自检？","25.2 自检 Checklist","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 12：任务单","考试实操 13：样本表设计","考试实操 14：小样本试跑","考试实操 15：操作步骤","考试实操 16：质量检查清单","考试实操 17：错例分析","考试实操 18：智慧图书馆应用","考试实操 19：评分细则"],"summary":"标注质量自检 从核心概念到训练师实战 标注与质检 · 第 25 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《标注质量自检》？…"},{"id":"lesson-026","number":26,"title":"文本标注综合实训","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第26课_文本标注综合实训.md","pptxPath":"pptx/A3_A4_标注与质检/第26课_文本标注综合实训.pptx","headings":["文本标注综合实训","从核心概念到训练师实战","为什么学习《文本标注综合实训》？","学习目标","26.1 实训任务","27-32 课：图像标注（详见图像标注分册）","33-36 课：语音标注（详见语音标注分册）","模块 A4：数据质量检查（16 课时）","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 11：任务单","考试实操 12：样本表设计","考试实操 13：小样本试跑","考试实操 14：操作步骤","考试实操 15：质量检查清单","考试实操 16：错例分析"],"summary":"文本标注综合实训 从核心概念到训练师实战 标注与质检 · 第 26 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《文本标注综合实训》？…"},{"id":"lesson-027","number":27,"title":"图像分类标注","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第27课_图像分类标注.md","pptxPath":"pptx/A3_A4_标注与质检/第27课_图像分类标注.pptx","headings":["图像分类标注","从核心概念到训练师实战","为什么学习《图像分类标注》？","学习目标","课程导航","考试实操 5：任务单","考试实操 6：样本表设计","考试实操 7：小样本试跑","考试实操 8：操作步骤","考试实操 9：质量检查清单","考试实操 10：错例分析","考试实操 11：智慧图书馆应用","考试实操 12：评分细则","考试实操 13：提交包结构","考试实操 14：限时训练","考试实操 15：口头答辩题","考试实操 16：任务单","考试实操 17：样本表设计","考试实操 18：小样本试跑","考试实操 19：操作步骤"],"summary":"图像分类标注 从核心概念到训练师实战 · 第 27 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 强化实操 · 完成交付 为什么学习《图像分类标注》？…"},{"id":"lesson-028","number":28,"title":"目标检测标注","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第28课_目标检测标注.md","pptxPath":"pptx/A3_A4_标注与质检/第28课_目标检测标注.pptx","headings":["目标检测标注","从核心概念到训练师实战","为什么学习《目标检测标注》？","学习目标","课程导航","考试实操 5：任务单","考试实操 6：样本表设计","考试实操 7：小样本试跑","考试实操 8：操作步骤","考试实操 9：质量检查清单","考试实操 10：错例分析","考试实操 11：智慧图书馆应用","考试实操 12：评分细则","考试实操 13：提交包结构","考试实操 14：限时训练","考试实操 15：口头答辩题","考试实操 16：任务单","考试实操 17：样本表设计","考试实操 18：小样本试跑","考试实操 19：操作步骤"],"summary":"目标检测标注 从核心概念到训练师实战 · 第 28 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 强化实操 · 完成交付 为什么学习《目标检测标注》？…"},{"id":"lesson-029","number":29,"title":"图像分割标注","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第29课_图像分割标注.md","pptxPath":"pptx/A3_A4_标注与质检/第29课_图像分割标注.pptx","headings":["图像分割标注","从核心概念到训练师实战","为什么学习《图像分割标注》？","学习目标","课程导航","考试实操 5：任务单","考试实操 6：样本表设计","考试实操 7：小样本试跑","考试实操 8：操作步骤","考试实操 9：质量检查清单","考试实操 10：错例分析","考试实操 11：智慧图书馆应用","考试实操 12：评分细则","考试实操 13：提交包结构","考试实操 14：限时训练","考试实操 15：口头答辩题","考试实操 16：任务单","考试实操 17：样本表设计","考试实操 18：小样本试跑","考试实操 19：操作步骤"],"summary":"图像分割标注 从核心概念到训练师实战 · 第 29 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 强化实操 · 完成交付 为什么学习《图像分割标注》？…"},{"id":"lesson-030","number":30,"title":"关键点标注","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第30课_关键点标注.md","pptxPath":"pptx/A3_A4_标注与质检/第30课_关键点标注.pptx","headings":["关键点标注","从核心概念到训练师实战","为什么学习《关键点标注》？","学习目标","课程导航","考试实操 5：任务单","考试实操 6：样本表设计","考试实操 7：小样本试跑","考试实操 8：操作步骤","考试实操 9：质量检查清单","考试实操 10：错例分析","考试实操 11：智慧图书馆应用","考试实操 12：评分细则","考试实操 13：提交包结构","考试实操 14：限时训练","考试实操 15：口头答辩题","考试实操 16：任务单","考试实操 17：样本表设计","考试实操 18：小样本试跑","考试实操 19：操作步骤"],"summary":"关键点标注 从核心概念到训练师实战 · 第 30 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 强化实操 · 完成交付 为什么学习《关键点标注》？…"},{"id":"lesson-031","number":31,"title":"OCR文本转写标注","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第31课_OCR文本转写标注.md","pptxPath":"pptx/A3_A4_标注与质检/第31课_OCR文本转写标注.pptx","headings":["OCR文本转写标注","从核心概念到训练师实战","为什么学习《OCR文本转写标注》？","学习目标","课程导航","考试实操 5：任务单","考试实操 6：样本表设计","考试实操 7：小样本试跑","考试实操 8：操作步骤","考试实操 9：质量检查清单","考试实操 10：错例分析","考试实操 11：智慧图书馆应用","考试实操 12：评分细则","考试实操 13：提交包结构","考试实操 14：限时训练","考试实操 15：口头答辩题","考试实操 16：任务单","考试实操 17：样本表设计","考试实操 18：小样本试跑","考试实操 19：操作步骤"],"summary":"OCR文本转写标注 从核心概念到训练师实战 · 第 31 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 强化实操 · 完成交付 为什么学习《OCR文本转写标注》？…"},{"id":"lesson-032","number":32,"title":"语音转写标注","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第32课_语音转写标注.md","pptxPath":"pptx/A3_A4_标注与质检/第32课_语音转写标注.pptx","headings":["语音转写标注","从核心概念到训练师实战","为什么学习《语音转写标注》？","学习目标","课程导航","考试实操 5：任务单","考试实操 6：样本表设计","考试实操 7：小样本试跑","考试实操 8：操作步骤","考试实操 9：质量检查清单","考试实操 10：错例分析","考试实操 11：智慧图书馆应用","考试实操 12：评分细则","考试实操 13：提交包结构","考试实操 14：限时训练","考试实操 15：口头答辩题","考试实操 16：任务单","考试实操 17：样本表设计","考试实操 18：小样本试跑","考试实操 19：操作步骤"],"summary":"语音转写标注 从核心概念到训练师实战 · 第 32 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 强化实操 · 完成交付 为什么学习《语音转写标注》？…"},{"id":"lesson-033","number":33,"title":"说话人分离与VAD标注","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第33课_说话人分离与VAD标注.md","pptxPath":"pptx/A3_A4_标注与质检/第33课_说话人分离与VAD标注.pptx","headings":["说话人分离与VAD标注","从核心概念到训练师实战","为什么学习《说话人分离与VAD标注》？","学习目标","课程导航","考试实操 5：任务单","考试实操 6：样本表设计","考试实操 7：小样本试跑","考试实操 8：操作步骤","考试实操 9：质量检查清单","考试实操 10：错例分析","考试实操 11：智慧图书馆应用","考试实操 12：评分细则","考试实操 13：提交包结构","考试实操 14：限时训练","考试实操 15：口头答辩题","考试实操 16：任务单","考试实操 17：样本表设计","考试实操 18：小样本试跑","考试实操 19：操作步骤"],"summary":"说话人分离与VAD标注 从核心概念到训练师实战 · 第 33 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 强化实操 · 完成交付 为什么学习《说话人分离与VAD标注》？…"},{"id":"lesson-034","number":34,"title":"多模态标注综合实训","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第34课_多模态标注综合实训.md","pptxPath":"pptx/A3_A4_标注与质检/第34课_多模态标注综合实训.pptx","headings":["多模态标注综合实训","从核心概念到训练师实战","为什么学习《多模态标注综合实训》？","学习目标","课程导航","考试实操 5：任务单","考试实操 6：样本表设计","考试实操 7：小样本试跑","考试实操 8：操作步骤","考试实操 9：质量检查清单","考试实操 10：错例分析","考试实操 11：智慧图书馆应用","考试实操 12：评分细则","考试实操 13：提交包结构","考试实操 14：限时训练","考试实操 15：口头答辩题","考试实操 16：任务单","考试实操 17：样本表设计","考试实操 18：小样本试跑","考试实操 19：操作步骤"],"summary":"多模态标注综合实训 从核心概念到训练师实战 · 第 34 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 强化实操 · 完成交付 为什么学习《多模态标注综合实训》？…"},{"id":"lesson-035","number":35,"title":"标注一致性评估 Kappa","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第35课_标注一致性评估_Kappa.md","pptxPath":"pptx/A3_A4_标注与质检/第35课_标注一致性评估_Kappa.pptx","headings":["标注一致性评估_Kappa","从核心概念到训练师实战","为什么学习《标注一致性评估_Kappa》？","学习目标","课程导航","考试实操 5：任务单","考试实操 6：样本表设计","考试实操 7：小样本试跑","考试实操 8：操作步骤","考试实操 9：质量检查清单","考试实操 10：错例分析","考试实操 11：智慧图书馆应用","考试实操 12：评分细则","考试实操 13：提交包结构","考试实操 14：限时训练","考试实操 15：口头答辩题","考试实操 16：任务单","考试实操 17：样本表设计","考试实操 18：小样本试跑","考试实操 19：操作步骤"],"summary":"标注一致性评估Kappa 从核心概念到训练师实战 · 第 35 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 强化实操 · 完成交付 为什么学习《标注一致性评估Kappa》？…"},{"id":"lesson-036","number":36,"title":"质检抽样与复核流程","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第36课_质检抽样与复核流程.md","pptxPath":"pptx/A3_A4_标注与质检/第36课_质检抽样与复核流程.pptx","headings":["质检抽样与复核流程","从核心概念到训练师实战","为什么学习《质检抽样与复核流程》？","学习目标","课程导航","考试实操 5：任务单","考试实操 6：样本表设计","考试实操 7：小样本试跑","考试实操 8：操作步骤","考试实操 9：质量检查清单","考试实操 10：错例分析","考试实操 11：智慧图书馆应用","考试实操 12：评分细则","考试实操 13：提交包结构","考试实操 14：限时训练","考试实操 15：口头答辩题","考试实操 16：任务单","考试实操 17：样本表设计","考试实操 18：小样本试跑","考试实操 19：操作步骤"],"summary":"质检抽样与复核流程 从核心概念到训练师实战 · 第 36 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 强化实操 · 完成交付 为什么学习《质检抽样与复核流程》？…"},{"id":"lesson-037","number":37,"title":"准确率指标","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第37课_准确率指标.md","pptxPath":"pptx/A3_A4_标注与质检/第37课_准确率指标.pptx","headings":["准确率指标","从核心概念到训练师实战","为什么学习《准确率指标》？","学习目标","37.1 标注准确率的计算","37.2 准确率偏低的常见原因","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 12：任务单","考试实操 13：样本表设计","考试实操 14：小样本试跑","考试实操 15：操作步骤","考试实操 16：质量检查清单","考试实操 17：错例分析","考试实操 18：智慧图书馆应用","考试实操 19：评分细则"],"summary":"准确率指标 从核心概念到训练师实战 标注与质检 · 第 37 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《准确率指标》？…"},{"id":"lesson-038","number":38,"title":"完整率与一致性","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第38课_完整率与一致性.md","pptxPath":"pptx/A3_A4_标注与质检/第38课_完整率与一致性.pptx","headings":["完整率与一致性","从核心概念到训练师实战","为什么学习《完整率与一致性》？","学习目标","38.1 完整率","38.2 标注一致性（Kappa 系数）","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 12：任务单","考试实操 13：样本表设计","考试实操 14：小样本试跑","考试实操 15：操作步骤","考试实操 16：质量检查清单","考试实操 17：错例分析","考试实操 18：智慧图书馆应用","考试实操 19：评分细则"],"summary":"完整率与一致性 从核心概念到训练师实战 标注与质检 · 第 38 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《完整率与一致性》？…"},{"id":"lesson-039","number":39,"title":"数据质量报告撰写","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第39课_数据质量报告撰写.md","pptxPath":"pptx/A3_A4_标注与质检/第39课_数据质量报告撰写.pptx","headings":["第 39 课  数据质量报告撰写","A3_A4_标注与质检 · 五级/初级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：标注与质检：AI训练师的基本功","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 39 课 数据质量报告撰写 A3A4标注与质检 · 五级/初级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 数据质量报告撰写，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流…"},{"id":"lesson-040","number":40,"title":"批量化修正","moduleFolder":"A3_A4_标注与质检","moduleCode":"A3/A4","moduleTitle":"标注与质检","level":"五级/初级","color":"#F2994A","hours":2,"relativePath":"A3_A4_标注与质检/第40课_批量化修正.md","pptxPath":"pptx/A3_A4_标注与质检/第40课_批量化修正.pptx","headings":["批量化修正","从核心概念到训练师实战","为什么学习《批量化修正》？","学习目标","40.1 批量化修正方法","40.2 修改原则","五级考核准备（2 课时）","五级考核方式","五级模块全部完成","已完成的课程（80 课时）","下一阶段：四级／中级工","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 12：任务单","考试实操 13：样本表设计","考试实操 14：小样本试跑"],"summary":"批量化修正 从核心概念到训练师实战 标注与质检 · 第 40 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《批量化修正》？…"},{"id":"lesson-041","number":41,"title":"Python环境搭建","moduleFolder":"B1_Python数据分析","moduleCode":"B1","moduleTitle":"Python数据分析","level":"四级/中级","color":"#00D9FF","hours":2,"relativePath":"B1_Python数据分析/第41课_Python环境搭建.md","pptxPath":"pptx/B1_Python数据分析/第41课_Python环境搭建.pptx","headings":["第 41 课  Python环境搭建","B1_Python数据分析 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：Python数据分析：让数据处理可复现","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 41 课 Python环境搭建 B1Python数据分析 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 Python环境搭建，解决真实项目中的三个问题： 1. 概念边界不清；…"},{"id":"lesson-042","number":42,"title":"Python变量、类型与控制流","moduleFolder":"B1_Python数据分析","moduleCode":"B1","moduleTitle":"Python数据分析","level":"四级/中级","color":"#00D9FF","hours":2,"relativePath":"B1_Python数据分析/第42课_Python变量、类型与控制流.md","pptxPath":"pptx/B1_Python数据分析/第42课_Python变量、类型与控制流.pptx","headings":["第 42 课  Python变量、类型与控制流","B1_Python数据分析 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：Python数据分析：让数据处理可复现","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 42 课 Python变量、类型与控制流 B1Python数据分析 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 Python变量、类型与控制流，解决真实项目中的三个问题： …"},{"id":"lesson-043","number":43,"title":"函数与模块","moduleFolder":"B1_Python数据分析","moduleCode":"B1","moduleTitle":"Python数据分析","level":"四级/中级","color":"#00D9FF","hours":2,"relativePath":"B1_Python数据分析/第43课_函数与模块.md","pptxPath":"pptx/B1_Python数据分析/第43课_函数与模块.pptx","headings":["第 43 课  函数与模块","B1_Python数据分析 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：Python数据分析：让数据处理可复现","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 43 课 函数与模块 B1Python数据分析 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 函数与模块，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流程不稳…"},{"id":"lesson-044","number":44,"title":"文件读写：TXT、CSV、JSON","moduleFolder":"B1_Python数据分析","moduleCode":"B1","moduleTitle":"Python数据分析","level":"四级/中级","color":"#00D9FF","hours":2,"relativePath":"B1_Python数据分析/第44课_文件读写：TXT、CSV、JSON.md","pptxPath":"pptx/B1_Python数据分析/第44课_文件读写：TXT、CSV、JSON.pptx","headings":["第 44 课  文件读写：TXT、CSV、JSON","B1_Python数据分析 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：Python数据分析：让数据处理可复现","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 44 课 文件读写：TXT、CSV、JSON B1Python数据分析 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 文件读写：TXT、CSV、JSON，解决真实项目中的三个…"},{"id":"lesson-045","number":45,"title":"异常处理与调试","moduleFolder":"B1_Python数据分析","moduleCode":"B1","moduleTitle":"Python数据分析","level":"四级/中级","color":"#00D9FF","hours":2,"relativePath":"B1_Python数据分析/第45课_异常处理与调试.md","pptxPath":"pptx/B1_Python数据分析/第45课_异常处理与调试.pptx","headings":["第 45 课  异常处理与调试","B1_Python数据分析 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：Python数据分析：让数据处理可复现","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 45 课 异常处理与调试 B1Python数据分析 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 异常处理与调试，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作…"},{"id":"lesson-046","number":46,"title":"批量文件处理实训","moduleFolder":"B1_Python数据分析","moduleCode":"B1","moduleTitle":"Python数据分析","level":"四级/中级","color":"#00D9FF","hours":2,"relativePath":"B1_Python数据分析/第46课_批量文件处理实训.md","pptxPath":"pptx/B1_Python数据分析/第46课_批量文件处理实训.pptx","headings":["第 46 课  批量文件处理实训","B1_Python数据分析 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：Python数据分析：让数据处理可复现","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 46 课 批量文件处理实训 B1Python数据分析 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 批量文件处理实训，解决真实项目中的三个问题： 1. 概念边界不清； 2. …"},{"id":"lesson-047","number":47,"title":"Python小项目：标注文件清洗","moduleFolder":"B1_Python数据分析","moduleCode":"B1","moduleTitle":"Python数据分析","level":"四级/中级","color":"#00D9FF","hours":2,"relativePath":"B1_Python数据分析/第47课_Python小项目：标注文件清洗.md","pptxPath":"pptx/B1_Python数据分析/第47课_Python小项目：标注文件清洗.pptx","headings":["第 47 课  Python小项目：标注文件清洗","B1_Python数据分析 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：Python数据分析：让数据处理可复现","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 47 课 Python小项目：标注文件清洗 B1Python数据分析 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 Python小项目：标注文件清洗，解决真实项目中的三个问题…"},{"id":"lesson-048","number":48,"title":"NumPy数组基础","moduleFolder":"B1_Python数据分析","moduleCode":"B1","moduleTitle":"Python数据分析","level":"四级/中级","color":"#00D9FF","hours":2,"relativePath":"B1_Python数据分析/第48课_NumPy数组基础.md","pptxPath":"pptx/B1_Python数据分析/第48课_NumPy数组基础.pptx","headings":["第 48 课  NumPy数组基础","B1_Python数据分析 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：Python数据分析：让数据处理可复现","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 48 课 NumPy数组基础 B1Python数据分析 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 NumPy数组基础，解决真实项目中的三个问题： 1. 概念边界不清； 2…"},{"id":"lesson-049","number":49,"title":"Pandas DataFrame基础","moduleFolder":"B1_Python数据分析","moduleCode":"B1","moduleTitle":"Python数据分析","level":"四级/中级","color":"#00D9FF","hours":2,"relativePath":"B1_Python数据分析/第49课_Pandas_DataFrame基础.md","pptxPath":"pptx/B1_Python数据分析/第49课_Pandas_DataFrame基础.pptx","headings":["第 49 课  Pandas DataFrame基础","B1_Python数据分析 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：Python数据分析：让数据处理可复现","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 49 课 Pandas DataFrame基础 B1Python数据分析 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 Pandas DataFrame基础，解决真实项目中的…"},{"id":"lesson-050","number":50,"title":"Pandas筛选、分组与聚合","moduleFolder":"B1_Python数据分析","moduleCode":"B1","moduleTitle":"Python数据分析","level":"四级/中级","color":"#00D9FF","hours":2,"relativePath":"B1_Python数据分析/第50课_Pandas筛选、分组与聚合.md","pptxPath":"pptx/B1_Python数据分析/第50课_Pandas筛选、分组与聚合.pptx","headings":["第 50 课  Pandas筛选、分组与聚合","B1_Python数据分析 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：Python数据分析：让数据处理可复现","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 50 课 Pandas筛选、分组与聚合 B1Python数据分析 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 Pandas筛选、分组与聚合，解决真实项目中的三个问题： 1.…"},{"id":"lesson-051","number":51,"title":"Matplotlib数据可视化","moduleFolder":"B1_Python数据分析","moduleCode":"B1","moduleTitle":"Python数据分析","level":"四级/中级","color":"#00D9FF","hours":2,"relativePath":"B1_Python数据分析/第51课_Matplotlib数据可视化.md","pptxPath":"pptx/B1_Python数据分析/第51课_Matplotlib数据可视化.pptx","headings":["第 51 课  Matplotlib数据可视化","B1_Python数据分析 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：Python数据分析：让数据处理可复现","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 51 课 Matplotlib数据可视化 B1Python数据分析 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 Matplotlib数据可视化，解决真实项目中的三个问题： …"},{"id":"lesson-052","number":52,"title":"EDA数据探索报告","moduleFolder":"B1_Python数据分析","moduleCode":"B1","moduleTitle":"Python数据分析","level":"四级/中级","color":"#00D9FF","hours":2,"relativePath":"B1_Python数据分析/第52课_EDA数据探索报告.md","pptxPath":"pptx/B1_Python数据分析/第52课_EDA数据探索报告.pptx","headings":["第 52 课  EDA数据探索报告","B1_Python数据分析 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：Python数据分析：让数据处理可复现","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 52 课 EDA数据探索报告 B1Python数据分析 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 EDA数据探索报告，解决真实项目中的三个问题： 1. 概念边界不清； 2…"},{"id":"lesson-053","number":53,"title":"文本数据增强","moduleFolder":"B2_数据处理进阶","moduleCode":"B2","moduleTitle":"数据处理进阶","level":"四级/中级","color":"#2ECC71","hours":2,"relativePath":"B2_数据处理进阶/第53课_文本数据增强.md","pptxPath":"pptx/B2_数据处理进阶/第53课_文本数据增强.pptx","headings":["第 53 课  文本数据增强","B2_数据处理进阶 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：进阶数据处理：多模态与多语种场景","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 53 课 文本数据增强 B2数据处理进阶 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 文本数据增强，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流程不稳； …"},{"id":"lesson-054","number":54,"title":"图像数据增强","moduleFolder":"B2_数据处理进阶","moduleCode":"B2","moduleTitle":"数据处理进阶","level":"四级/中级","color":"#2ECC71","hours":2,"relativePath":"B2_数据处理进阶/第54课_图像数据增强.md","pptxPath":"pptx/B2_数据处理进阶/第54课_图像数据增强.pptx","headings":["第 54 课  图像数据增强","B2_数据处理进阶 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：进阶数据处理：多模态与多语种场景","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 54 课 图像数据增强 B2数据处理进阶 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 图像数据增强，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流程不稳； …"},{"id":"lesson-055","number":55,"title":"语音数据增强","moduleFolder":"B2_数据处理进阶","moduleCode":"B2","moduleTitle":"数据处理进阶","level":"四级/中级","color":"#2ECC71","hours":2,"relativePath":"B2_数据处理进阶/第55课_语音数据增强.md","pptxPath":"pptx/B2_数据处理进阶/第55课_语音数据增强.pptx","headings":["第 55 课  语音数据增强","B2_数据处理进阶 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：进阶数据处理：多模态与多语种场景","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 55 课 语音数据增强 B2数据处理进阶 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 语音数据增强，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流程不稳； …"},{"id":"lesson-056","number":56,"title":"数据增强伦理与边界","moduleFolder":"B2_数据处理进阶","moduleCode":"B2","moduleTitle":"数据处理进阶","level":"四级/中级","color":"#2ECC71","hours":2,"relativePath":"B2_数据处理进阶/第56课_数据增强伦理与边界.md","pptxPath":"pptx/B2_数据处理进阶/第56课_数据增强伦理与边界.pptx","headings":["第 56 课  数据增强伦理与边界","B2_数据处理进阶 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：进阶数据处理：多模态与多语种场景","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 56 课 数据增强伦理与边界 B2数据处理进阶 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 数据增强伦理与边界，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作…"},{"id":"lesson-057","number":57,"title":"Label Studio部署","moduleFolder":"B2_数据处理进阶","moduleCode":"B2","moduleTitle":"数据处理进阶","level":"四级/中级","color":"#2ECC71","hours":2,"relativePath":"B2_数据处理进阶/第57课_Label_Studio部署.md","pptxPath":"pptx/B2_数据处理进阶/第57课_Label_Studio部署.pptx","headings":["第 57 课  Label Studio部署","B2_数据处理进阶 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：进阶数据处理：多模态与多语种场景","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 57 课 Label Studio部署 B2数据处理进阶 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 Label Studio部署，解决真实项目中的三个问题： 1. 概念边…"},{"id":"lesson-058","number":58,"title":"标注项目配置","moduleFolder":"B2_数据处理进阶","moduleCode":"B2","moduleTitle":"数据处理进阶","level":"四级/中级","color":"#2ECC71","hours":2,"relativePath":"B2_数据处理进阶/第58课_标注项目配置.md","pptxPath":"pptx/B2_数据处理进阶/第58课_标注项目配置.pptx","headings":["第 58 课  标注项目配置","B2_数据处理进阶 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：进阶数据处理：多模态与多语种场景","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 58 课 标注项目配置 B2数据处理进阶 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 标注项目配置，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流程不稳； …"},{"id":"lesson-059","number":59,"title":"标注任务分配与进度管理","moduleFolder":"B2_数据处理进阶","moduleCode":"B2","moduleTitle":"数据处理进阶","level":"四级/中级","color":"#2ECC71","hours":2,"relativePath":"B2_数据处理进阶/第59课_标注任务分配与进度管理.md","pptxPath":"pptx/B2_数据处理进阶/第59课_标注任务分配与进度管理.pptx","headings":["第 59 课  标注任务分配与进度管理","B2_数据处理进阶 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：进阶数据处理：多模态与多语种场景","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 59 课 标注任务分配与进度管理 B2数据处理进阶 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 标注任务分配与进度管理，解决真实项目中的三个问题： 1. 概念边界不清； 2…"},{"id":"lesson-060","number":60,"title":"标注数据导入导出","moduleFolder":"B2_数据处理进阶","moduleCode":"B2","moduleTitle":"数据处理进阶","level":"四级/中级","color":"#2ECC71","hours":2,"relativePath":"B2_数据处理进阶/第60课_标注数据导入导出.md","pptxPath":"pptx/B2_数据处理进阶/第60课_标注数据导入导出.pptx","headings":["第 60 课  标注数据导入导出","B2_数据处理进阶 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：进阶数据处理：多模态与多语种场景","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 60 课 标注数据导入导出 B2数据处理进阶 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 标注数据导入导出，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流程…"},{"id":"lesson-061","number":61,"title":"Unicode与多语文本处理","moduleFolder":"B2_数据处理进阶","moduleCode":"B2","moduleTitle":"数据处理进阶","level":"四级/中级","color":"#2ECC71","hours":2,"relativePath":"B2_数据处理进阶/第61课_Unicode与多语文本处理.md","pptxPath":"pptx/B2_数据处理进阶/第61课_Unicode与多语文本处理.pptx","headings":["第 61 课  Unicode与多语文本处理","B2_数据处理进阶 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：进阶数据处理：多模态与多语种场景","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 61 课 Unicode与多语文本处理 B2数据处理进阶 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 Unicode与多语文本处理，解决真实项目中的三个问题： 1. 概念边…"},{"id":"lesson-062","number":62,"title":"中英阿文本清洗差异","moduleFolder":"B2_数据处理进阶","moduleCode":"B2","moduleTitle":"数据处理进阶","level":"四级/中级","color":"#2ECC71","hours":2,"relativePath":"B2_数据处理进阶/第62课_中英阿文本清洗差异.md","pptxPath":"pptx/B2_数据处理进阶/第62课_中英阿文本清洗差异.pptx","headings":["第 62 课  中英阿文本清洗差异","B2_数据处理进阶 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：进阶数据处理：多模态与多语种场景","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 62 课 中英阿文本清洗差异 B2数据处理进阶 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 中英阿文本清洗差异，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作…"},{"id":"lesson-063","number":63,"title":"跨语言标注规范","moduleFolder":"B2_数据处理进阶","moduleCode":"B2","moduleTitle":"数据处理进阶","level":"四级/中级","color":"#2ECC71","hours":2,"relativePath":"B2_数据处理进阶/第63课_跨语言标注规范.md","pptxPath":"pptx/B2_数据处理进阶/第63课_跨语言标注规范.pptx","headings":["第 63 课  跨语言标注规范","B2_数据处理进阶 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：进阶数据处理：多模态与多语种场景","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 63 课 跨语言标注规范 B2数据处理进阶 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 跨语言标注规范，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流程不稳…"},{"id":"lesson-064","number":64,"title":"多语种OCR标注","moduleFolder":"B2_数据处理进阶","moduleCode":"B2","moduleTitle":"数据处理进阶","level":"四级/中级","color":"#2ECC71","hours":2,"relativePath":"B2_数据处理进阶/第64课_多语种OCR标注.md","pptxPath":"pptx/B2_数据处理进阶/第64课_多语种OCR标注.pptx","headings":["第 64 课  多语种OCR标注","B2_数据处理进阶 · 四级/中级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：进阶数据处理：多模态与多语种场景","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 64 课 多语种OCR标注 B2数据处理进阶 · 四级/中级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 多语种OCR标注，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流程…"},{"id":"lesson-065","number":65,"title":"三大学习范式","moduleFolder":"B3_模型训练基础","moduleCode":"B3","moduleTitle":"模型训练基础","level":"四级/中级","color":"#F2C94C","hours":2,"relativePath":"B3_模型训练基础/第65课_三大学习范式.md","pptxPath":"pptx/B3_模型训练基础/第65课_三大学习范式.pptx","headings":["三大学习范式","从核心概念到训练师实战","为什么学习《三大学习范式》？","学习目标","65.1 监督学习","65.2 无监督学习","65.3 半监督学习","65.4 课堂练习","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 14：任务单","考试实操 15：样本表设计","考试实操 16：小样本试跑","考试实操 17：操作步骤","考试实操 18：质量检查清单","考试实操 19：错例分析"],"summary":"三大学习范式 从核心概念到训练师实战 模型训练基础 · 第 65 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《三大学习范式》？…"},{"id":"lesson-066","number":66,"title":"分类算法","moduleFolder":"B3_模型训练基础","moduleCode":"B3","moduleTitle":"模型训练基础","level":"四级/中级","color":"#F2C94C","hours":2,"relativePath":"B3_模型训练基础/第66课_分类算法.md","pptxPath":"pptx/B3_模型训练基础/第66课_分类算法.pptx","headings":["分类算法","从核心概念到训练师实战","为什么学习《分类算法》？","学习目标","66.1 逻辑回归","预测","66.2 决策树","66.3 分类实战：情感分类","1. 文本向量化 + 分类","2. 训练","3. 预测","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 13：任务单","考试实操 14：样本表设计","考试实操 15：小样本试跑"],"summary":"分类算法 从核心概念到训练师实战 模型训练基础 · 第 66 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《分类算法》？…"},{"id":"lesson-067","number":67,"title":"回归算法","moduleFolder":"B3_模型训练基础","moduleCode":"B3","moduleTitle":"模型训练基础","level":"四级/中级","color":"#F2C94C","hours":2,"relativePath":"B3_模型训练基础/第67课_回归算法.md","pptxPath":"pptx/B3_模型训练基础/第67课_回归算法.pptx","headings":["回归算法","从核心概念到训练师实战","为什么学习《回归算法》？","学习目标","67.1 什么是回归？","67.2 线性回归","数据：标注员经验天数 → 日标注量","预测：第 40 天能标多少？","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 12：任务单","考试实操 13：样本表设计","考试实操 14：小样本试跑","考试实操 15：操作步骤","考试实操 16：质量检查清单","考试实操 17：错例分析"],"summary":"回归算法 从核心概念到训练师实战 模型训练基础 · 第 67 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《回归算法》？…"},{"id":"lesson-068","number":68,"title":"聚类算法","moduleFolder":"B3_模型训练基础","moduleCode":"B3","moduleTitle":"模型训练基础","level":"四级/中级","color":"#F2C94C","hours":2,"relativePath":"B3_模型训练基础/第68课_聚类算法.md","pptxPath":"pptx/B3_模型训练基础/第68课_聚类算法.pptx","headings":["聚类算法","从核心概念到训练师实战","为什么学习《聚类算法》？","学习目标","68.1 K-Means 聚类","标注员效率数据","聚成 2 类","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 11：任务单","考试实操 12：样本表设计","考试实操 13：小样本试跑","考试实操 14：操作步骤","考试实操 15：质量检查清单","考试实操 16：错例分析","考试实操 17：智慧图书馆应用"],"summary":"聚类算法 从核心概念到训练师实战 模型训练基础 · 第 68 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《聚类算法》？…"},{"id":"lesson-069","number":69,"title":"过拟合与欠拟合","moduleFolder":"B3_模型训练基础","moduleCode":"B3","moduleTitle":"模型训练基础","level":"四级/中级","color":"#F2C94C","hours":2,"relativePath":"B3_模型训练基础/第69课_过拟合与欠拟合.md","pptxPath":"pptx/B3_模型训练基础/第69课_过拟合与欠拟合.pptx","headings":["过拟合与欠拟合","从核心概念到训练师实战","为什么学习《过拟合与欠拟合》？","学习目标","69.1 什么是过拟合？","69.2 解决过拟合的方法","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 12：任务单","考试实操 13：样本表设计","考试实操 14：小样本试跑","考试实操 15：操作步骤","考试实操 16：质量检查清单","考试实操 17：错例分析","考试实操 18：智慧图书馆应用","考试实操 19：评分细则"],"summary":"过拟合与欠拟合 从核心概念到训练师实战 模型训练基础 · 第 69 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《过拟合与欠拟合》？…"},{"id":"lesson-070","number":70,"title":"模型评估指标","moduleFolder":"B3_模型训练基础","moduleCode":"B3","moduleTitle":"模型训练基础","level":"四级/中级","color":"#F2C94C","hours":2,"relativePath":"B3_模型训练基础/第70课_模型评估指标.md","pptxPath":"pptx/B3_模型训练基础/第70课_模型评估指标.pptx","headings":["模型评估指标","从核心概念到训练师实战","为什么学习《模型评估指标》？","学习目标","70.1 混淆矩阵","70.2 核心指标","70.3 在标注数据上的应用","标注员结果 vs 专家复核结果","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 13：任务单","考试实操 14：样本表设计","考试实操 15：小样本试跑","考试实操 16：操作步骤","考试实操 17：质量检查清单","考试实操 18：错例分析"],"summary":"模型评估指标 从核心概念到训练师实战 模型训练基础 · 第 70 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《模型评估指标》？…"},{"id":"lesson-071","number":71,"title":"PyTorch 入门","moduleFolder":"B3_模型训练基础","moduleCode":"B3","moduleTitle":"模型训练基础","level":"四级/中级","color":"#F2C94C","hours":2,"relativePath":"B3_模型训练基础/第71课_PyTorch_入门.md","pptxPath":"pptx/B3_模型训练基础/第71课_PyTorch_入门.pptx","headings":["PyTorch 入门","从核心概念到训练师实战","为什么学习《PyTorch 入门》？","学习目标","71.1 为什么用 PyTorch？","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 11：任务单","考试实操 12：样本表设计","考试实操 13：小样本试跑","考试实操 14：操作步骤","考试实操 15：质量检查清单","考试实操 16：错例分析","考试实操 17：智慧图书馆应用","考试实操 18：评分细则","考试实操 19：提交包结构"],"summary":"PyTorch 入门 从核心概念到训练师实战 模型训练基础 · 第 71 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《PyTorch 入门》？…"},{"id":"lesson-072","number":72,"title":"张量操作","moduleFolder":"B3_模型训练基础","moduleCode":"B3","moduleTitle":"模型训练基础","level":"四级/中级","color":"#F2C94C","hours":2,"relativePath":"B3_模型训练基础/第72课_张量操作.md","pptxPath":"pptx/B3_模型训练基础/第72课_张量操作.pptx","headings":["张量操作","从核心概念到训练师实战","为什么学习《张量操作》？","学习目标","72.1 什么是张量？","标量（0维）","向量（1维）","矩阵（2维）","3维张量（图像：C×H×W）","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 11：任务单","考试实操 12：样本表设计","考试实操 13：小样本试跑","考试实操 14：操作步骤","考试实操 15：质量检查清单"],"summary":"张量操作 从核心概念到训练师实战 模型训练基础 · 第 72 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《张量操作》？…"},{"id":"lesson-073","number":73,"title":"简单神经网络搭建","moduleFolder":"B3_模型训练基础","moduleCode":"B3","moduleTitle":"模型训练基础","level":"四级/中级","color":"#F2C94C","hours":2,"relativePath":"B3_模型训练基础/第73课_简单神经网络搭建.md","pptxPath":"pptx/B3_模型训练基础/第73课_简单神经网络搭建.pptx","headings":["简单神经网络搭建","从核心概念到训练师实战","为什么学习《简单神经网络搭建》？","学习目标","73.1 用 PyTorch 定义分类模型","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 11：任务单","考试实操 12：样本表设计","考试实操 13：小样本试跑","考试实操 14：操作步骤","考试实操 15：质量检查清单","考试实操 16：错例分析","考试实操 17：智慧图书馆应用","考试实操 18：评分细则","考试实操 19：提交包结构"],"summary":"简单神经网络搭建 从核心概念到训练师实战 模型训练基础 · 第 73 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《简单神经网络搭建》？…"},{"id":"lesson-074","number":74,"title":"训练流程完整代码","moduleFolder":"B3_模型训练基础","moduleCode":"B3","moduleTitle":"模型训练基础","level":"四级/中级","color":"#F2C94C","hours":2,"relativePath":"B3_模型训练基础/第74课_训练流程完整代码.md","pptxPath":"pptx/B3_模型训练基础/第74课_训练流程完整代码.pptx","headings":["训练流程完整代码","从核心概念到训练师实战","为什么学习《训练流程完整代码》？","学习目标","74.1 一次完整的训练","1. 准备数据（假设已标注）","2. 定义模型","3. 定义损失函数和优化器","4. 训练循环","第 75-76 课：参数配置与监控","75.1 关键训练参数","76.1 使用 TensorBoard 可视化","启动: tensorboard --logdir=runs","B3 模块总结","已完成的课程（24 课时）","下一模块：B4 模型评估与优化","动手实践","案例讨论","随堂测验","常见错误"],"summary":"训练流程完整代码 从核心概念到训练师实战 模型训练基础 · 第 74 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《训练流程完整代码》？…"},{"id":"lesson-075","number":75,"title":"训练结果可视化与日志分析","moduleFolder":"B3_模型训练基础","moduleCode":"B3","moduleTitle":"模型训练基础","level":"四级/中级","color":"#F2C94C","hours":2,"relativePath":"B3_模型训练基础/第75课_训练结果可视化与日志分析.md","pptxPath":"pptx/B3_模型训练基础/第75课_训练结果可视化与日志分析.pptx","headings":["训练结果可视化与日志分析","从核心概念到训练师实战","为什么学习《训练结果可视化与日志分析》？","学习目标","课程导航","考试实操 5：任务单","考试实操 6：样本表设计","考试实操 7：小样本试跑","考试实操 8：操作步骤","考试实操 9：质量检查清单","考试实操 10：错例分析","考试实操 11：智慧图书馆应用","考试实操 12：评分细则","考试实操 13：提交包结构","考试实操 14：限时训练","考试实操 15：口头答辩题","考试实操 16：任务单","考试实操 17：样本表设计","考试实操 18：小样本试跑","考试实操 19：操作步骤"],"summary":"训练结果可视化与日志分析 从核心概念到训练师实战 · 第 75 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 强化实操 · 完成交付 为什么学习《训练结果可视化与日志分析》？…"},{"id":"lesson-076","number":76,"title":"模型训练综合实训","moduleFolder":"B3_模型训练基础","moduleCode":"B3","moduleTitle":"模型训练基础","level":"四级/中级","color":"#F2C94C","hours":2,"relativePath":"B3_模型训练基础/第76课_模型训练综合实训.md","pptxPath":"pptx/B3_模型训练基础/第76课_模型训练综合实训.pptx","headings":["模型训练综合实训","从核心概念到训练师实战","为什么学习《模型训练综合实训》？","学习目标","课程导航","考试实操 5：任务单","考试实操 6：样本表设计","考试实操 7：小样本试跑","考试实操 8：操作步骤","考试实操 9：质量检查清单","考试实操 10：错例分析","考试实操 11：智慧图书馆应用","考试实操 12：评分细则","考试实操 13：提交包结构","考试实操 14：限时训练","考试实操 15：口头答辩题","考试实操 16：任务单","考试实操 17：样本表设计","考试实操 18：小样本试跑","考试实操 19：操作步骤"],"summary":"模型训练综合实训 从核心概念到训练师实战 · 第 76 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 强化实操 · 完成交付 为什么学习《模型训练综合实训》？…"},{"id":"lesson-077","number":77,"title":"决策树与随机森林","moduleFolder":"C1_机器学习深入","moduleCode":"C1","moduleTitle":"机器学习深入","level":"三级/高级","color":"#00D9FF","hours":2,"relativePath":"C1_机器学习深入/第77课_决策树与随机森林.md","pptxPath":"pptx/C1_机器学习深入/第77课_决策树与随机森林.pptx","headings":["决策树与随机森林","从核心概念到训练师实战","为什么学习《决策树与随机森林》？","学习目标","77.1 随机森林原理","特征重要性——可以告诉训练师\"哪些特征对模型最重要\"","77.2 特征重要性：标注指导价值","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 12：任务单","考试实操 13：样本表设计","考试实操 14：小样本试跑","考试实操 15：操作步骤","考试实操 16：质量检查清单","考试实操 17：错例分析","考试实操 18：智慧图书馆应用"],"summary":"决策树与随机森林 从核心概念到训练师实战 机器学习深入 · 第 77 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《决策树与随机森林》？…"},{"id":"lesson-078","number":78,"title":"SVM 与核方法","moduleFolder":"C1_机器学习深入","moduleCode":"C1","moduleTitle":"机器学习深入","level":"三级/高级","color":"#00D9FF","hours":2,"relativePath":"C1_机器学习深入/第78课_SVM_与核方法.md","pptxPath":"pptx/C1_机器学习深入/第78课_SVM_与核方法.pptx","headings":["SVM 与核方法","从核心概念到训练师实战","为什么学习《SVM 与核方法》？","学习目标","78.1 SVM 的核心思想","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 11：任务单","考试实操 12：样本表设计","考试实操 13：小样本试跑","考试实操 14：操作步骤","考试实操 15：质量检查清单","考试实操 16：错例分析","考试实操 17：智慧图书馆应用","考试实操 18：评分细则","考试实操 19：提交包结构"],"summary":"SVM 与核方法 从核心概念到训练师实战 机器学习深入 · 第 78 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《SVM 与核方法》？…"},{"id":"lesson-079","number":79,"title":"XGBoost LightGBM","moduleFolder":"C1_机器学习深入","moduleCode":"C1","moduleTitle":"机器学习深入","level":"三级/高级","color":"#00D9FF","hours":2,"relativePath":"C1_机器学习深入/第79课_XGBoost_LightGBM.md","pptxPath":"pptx/C1_机器学习深入/第79课_XGBoost_LightGBM.pptx","headings":["XGBoost /","从核心概念到训练师实战","为什么学习《XGBoost / LightGBM》？","学习目标","79.1 梯度提升树","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 11：任务单","考试实操 12：样本表设计","考试实操 13：小样本试跑","考试实操 14：操作步骤","考试实操 15：质量检查清单","考试实操 16：错例分析","考试实操 17：智慧图书馆应用","考试实操 18：评分细则","考试实操 19：提交包结构"],"summary":"XGBoost / LightGBM 从核心概念到训练师实战 机器学习深入 · 第 79 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《XGBoost / LightGBM》？…"},{"id":"lesson-080","number":80,"title":"集成学习方法","moduleFolder":"C1_机器学习深入","moduleCode":"C1","moduleTitle":"机器学习深入","level":"三级/高级","color":"#00D9FF","hours":2,"relativePath":"C1_机器学习深入/第80课_集成学习方法.md","pptxPath":"pptx/C1_机器学习深入/第80课_集成学习方法.pptx","headings":["集成学习方法","从核心概念到训练师实战","为什么学习《集成学习方法》？","学习目标","80.1 Bagging vs Boosting vs Stacking","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 11：任务单","考试实操 12：样本表设计","考试实操 13：小样本试跑","考试实操 14：操作步骤","考试实操 15：质量检查清单","考试实操 16：错例分析","考试实操 17：智慧图书馆应用","考试实操 18：评分细则","考试实操 19：提交包结构"],"summary":"集成学习方法 从核心概念到训练师实战 机器学习深入 · 第 80 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《集成学习方法》？…"},{"id":"lesson-081","number":81,"title":"神经网络原理","moduleFolder":"C1_机器学习深入","moduleCode":"C1","moduleTitle":"机器学习深入","level":"三级/高级","color":"#00D9FF","hours":2,"relativePath":"C1_机器学习深入/第81课_神经网络原理.md","pptxPath":"pptx/C1_机器学习深入/第81课_神经网络原理.pptx","headings":["神经网络原理","从核心概念到训练师实战","为什么学习《神经网络原理》？","学习目标","81.1 神经元：神经网络的基本单位","81.2 反向传播算法直觉","PyTorch 自动处理反向传播","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 12：任务单","考试实操 13：样本表设计","考试实操 14：小样本试跑","考试实操 15：操作步骤","考试实操 16：质量检查清单","考试实操 17：错例分析","考试实操 18：智慧图书馆应用"],"summary":"神经网络原理 从核心概念到训练师实战 机器学习深入 · 第 81 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《神经网络原理》？…"},{"id":"lesson-082","number":82,"title":"CNN 基础与实践","moduleFolder":"C1_机器学习深入","moduleCode":"C1","moduleTitle":"机器学习深入","level":"三级/高级","color":"#00D9FF","hours":2,"relativePath":"C1_机器学习深入/第82课_CNN_基础与实践.md","pptxPath":"pptx/C1_机器学习深入/第82课_CNN_基础与实践.pptx","headings":["CNN 基础与实践","从核心概念到训练师实战","为什么学习《CNN 基础与实践》？","学习目标","82.1 卷积操作","定义 CNN 图像分类器","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 11：任务单","考试实操 12：样本表设计","考试实操 13：小样本试跑","考试实操 14：操作步骤","考试实操 15：质量检查清单","考试实操 16：错例分析","考试实操 17：智慧图书馆应用","考试实操 18：评分细则"],"summary":"CNN 基础与实践 从核心概念到训练师实战 机器学习深入 · 第 82 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《CNN 基础与实践》？…"},{"id":"lesson-083","number":83,"title":"RNN LSTM Transformer","moduleFolder":"C1_机器学习深入","moduleCode":"C1","moduleTitle":"机器学习深入","level":"三级/高级","color":"#00D9FF","hours":2,"relativePath":"C1_机器学习深入/第83课_RNN_LSTM_Transformer.md","pptxPath":"pptx/C1_机器学习深入/第83课_RNN_LSTM_Transformer.pptx","headings":["RNN / LSTM /","从核心概念到训练师实战","为什么学习《RNN / LSTM / Transformer》？","学习目标","83.1 序列模型对比","83.2 Transformer 核心：注意力机制","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 12：任务单","考试实操 13：样本表设计","考试实操 14：小样本试跑","考试实操 15：操作步骤","考试实操 16：质量检查清单","考试实操 17：错例分析","考试实操 18：智慧图书馆应用","考试实操 19：评分细则"],"summary":"RNN / LSTM / Transformer 从核心概念到训练师实战 机器学习深入 · 第 83 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《RNN / LSTM / Transformer》？…"},{"id":"lesson-084","number":84,"title":"完整训练管线","moduleFolder":"C1_机器学习深入","moduleCode":"C1","moduleTitle":"机器学习深入","level":"三级/高级","color":"#00D9FF","hours":2,"relativePath":"C1_机器学习深入/第84课_完整训练管线.md","pptxPath":"pptx/C1_机器学习深入/第84课_完整训练管线.pptx","headings":["完整训练管线","从核心概念到训练师实战","为什么学习《完整训练管线》？","学习目标","84.1 从数据到模型的完整流程","---- 自定义数据集 ----","---- 数据加载 ----","---- 训练循环 ----","第 85-88 课：模型优化技术","85.1 学习率调度策略","Cosine Annealing（最常用）","ReduceLROnPlateau（当 loss 不再下降时降低 LR）","86.1 正则化技术总结","87.1 早停 Early Stopping","88.1 K-Fold 交叉验证","C1 模块总结（40 课时）","下一模块：C2 大模型训练与微调（48 课时）","动手实践","案例讨论","随堂测验"],"summary":"完整训练管线 从核心概念到训练师实战 机器学习深入 · 第 84 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《完整训练管线》？…"},{"id":"lesson-085","number":85,"title":"特征工程与降维","moduleFolder":"C1_机器学习深入","moduleCode":"C1","moduleTitle":"机器学习深入","level":"三级/高级","color":"#00D9FF","hours":2,"relativePath":"C1_机器学习深入/第85课_特征工程与降维.md","pptxPath":"pptx/C1_机器学习深入/第85课_特征工程与降维.pptx","headings":["特征工程与降维","从核心概念到训练师实战","为什么学习《特征工程与降维》？","学习目标","课程导航","考试实操 5：任务单","考试实操 6：样本表设计","考试实操 7：小样本试跑","考试实操 8：操作步骤","考试实操 9：质量检查清单","考试实操 10：错例分析","考试实操 11：智慧图书馆应用","考试实操 12：评分细则","考试实操 13：提交包结构","考试实操 14：限时训练","考试实操 15：口头答辩题","考试实操 16：任务单","考试实操 17：样本表设计","考试实操 18：小样本试跑","考试实操 19：操作步骤"],"summary":"特征工程与降维 从核心概念到训练师实战 · 第 85 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 强化实操 · 完成交付 为什么学习《特征工程与降维》？…"},{"id":"lesson-086","number":86,"title":"调优综合案例：从欠拟合到过拟合诊断","moduleFolder":"C1_机器学习深入","moduleCode":"C1","moduleTitle":"机器学习深入","level":"三级/高级","color":"#00D9FF","hours":2,"relativePath":"C1_机器学习深入/第86课_调优综合案例：从欠拟合到过拟合诊断.md","pptxPath":"pptx/C1_机器学习深入/第86课_调优综合案例：从欠拟合到过拟合诊断.pptx","headings":["第 86 课  调优综合案例：从欠拟合到过拟合诊断","C1_机器学习深入 · 三级/高级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：机器学习深入：从算法选择到可解释性","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 86 课 调优综合案例：从欠拟合到过拟合诊断 C1机器学习深入 · 三级/高级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 调优综合案例：从欠拟合到过拟合诊断，解决真实项目中的三个问题： …"},{"id":"lesson-087","number":87,"title":"模型解释性与公平性","moduleFolder":"C1_机器学习深入","moduleCode":"C1","moduleTitle":"机器学习深入","level":"三级/高级","color":"#00D9FF","hours":2,"relativePath":"C1_机器学习深入/第87课_模型解释性与公平性.md","pptxPath":"pptx/C1_机器学习深入/第87课_模型解释性与公平性.pptx","headings":["模型解释性与公平性","从核心概念到训练师实战","为什么学习《模型解释性与公平性》？","学习目标","课程导航","考试实操 5：任务单","考试实操 6：样本表设计","考试实操 7：小样本试跑","考试实操 8：操作步骤","考试实操 9：质量检查清单","考试实操 10：错例分析","考试实操 11：智慧图书馆应用","考试实操 12：评分细则","考试实操 13：提交包结构","考试实操 14：限时训练","考试实操 15：口头答辩题","考试实操 16：任务单","考试实操 17：样本表设计","考试实操 18：小样本试跑","考试实操 19：操作步骤"],"summary":"模型解释性与公平性 从核心概念到训练师实战 · 第 87 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 强化实操 · 完成交付 为什么学习《模型解释性与公平性》？…"},{"id":"lesson-088","number":88,"title":"机器学习综合项目实训","moduleFolder":"C1_机器学习深入","moduleCode":"C1","moduleTitle":"机器学习深入","level":"三级/高级","color":"#00D9FF","hours":2,"relativePath":"C1_机器学习深入/第88课_机器学习综合项目实训.md","pptxPath":"pptx/C1_机器学习深入/第88课_机器学习综合项目实训.pptx","headings":["机器学习综合项目实训","从核心概念到训练师实战","为什么学习《机器学习综合项目实训》？","学习目标","课程导航","考试实操 5：任务单","考试实操 6：样本表设计","考试实操 7：小样本试跑","考试实操 8：操作步骤","考试实操 9：质量检查清单","考试实操 10：错例分析","考试实操 11：智慧图书馆应用","考试实操 12：评分细则","考试实操 13：提交包结构","考试实操 14：限时训练","考试实操 15：口头答辩题","考试实操 16：任务单","考试实操 17：样本表设计","考试实操 18：小样本试跑","考试实操 19：操作步骤"],"summary":"机器学习综合项目实训 从核心概念到训练师实战 · 第 88 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 强化实操 · 完成交付 为什么学习《机器学习综合项目实训》？…"},{"id":"lesson-089","number":89,"title":"BERT 原理","moduleFolder":"C2_大模型训练与微调","moduleCode":"C2","moduleTitle":"大模型训练与微调","level":"三级/高级","color":"#238B9B","hours":2,"relativePath":"C2_大模型训练与微调/第89课_BERT_原理.md","pptxPath":"pptx/C2_大模型训练与微调/第89课_BERT_原理.pptx","headings":["BERT 原理","从核心概念到训练师实战","为什么学习《BERT 原理》？","学习目标","89.1 从 Word2Vec 到 BERT 的进化","89.2 BERT 的预训练任务","89.3 BERT 微调实践","加载预训练 BERT","标注数据 → BERT 输入格式","Tokenization","训练","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 13：任务单","考试实操 14：样本表设计","考试实操 15：小样本试跑"],"summary":"BERT 原理 从核心概念到训练师实战 大模型训练与微调 · 第 89 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《BERT 原理》？…"},{"id":"lesson-090","number":90,"title":"GPT LLaMA 系列","moduleFolder":"C2_大模型训练与微调","moduleCode":"C2","moduleTitle":"大模型训练与微调","level":"三级/高级","color":"#238B9B","hours":2,"relativePath":"C2_大模型训练与微调/第90课_GPT_LLaMA_系列.md","pptxPath":"pptx/C2_大模型训练与微调/第90课_GPT_LLaMA_系列.pptx","headings":["GPT / LLaMA 系列","从核心概念到训练师实战","为什么学习《GPT / LLaMA 系列》？","学习目标","90.1 GPT vs BERT：单向 vs 双向","90.2 LLaMA 系列的开源生态","90.3 LLaMA 本地部署","使用 Ollama（最简单方式）","Python 调用","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 13：任务单","考试实操 14：样本表设计","考试实操 15：小样本试跑","考试实操 16：操作步骤","考试实操 17：质量检查清单"],"summary":"GPT / LLaMA 系列 从核心概念到训练师实战 大模型训练与微调 · 第 90 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《GPT / LLaMA 系列》？…"},{"id":"lesson-091","number":91,"title":"多语言模型 北二外特色","moduleFolder":"C2_大模型训练与微调","moduleCode":"C2","moduleTitle":"大模型训练与微调","level":"三级/高级","color":"#238B9B","hours":2,"relativePath":"C2_大模型训练与微调/第91课_多语言模型_北二外特色.md","pptxPath":"pptx/C2_大模型训练与微调/第91课_多语言模型_北二外特色.pptx","headings":["多语言模型 ⭐（北二外特色）","从核心概念到训练师实战","为什么学习《多语言模型 ⭐（北二外特色）》？","学习目标","91.1 多语言 vs 单语言模型对比","91.2 LaBSE 实战：跨语言相似度","三语对齐示例","计算相似度","91.3 多语言标注数据的准备","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 13：任务单","考试实操 14：样本表设计","考试实操 15：小样本试跑","考试实操 16：操作步骤","考试实操 17：质量检查清单"],"summary":"多语言模型 ⭐（北二外特色） 从核心概念到训练师实战 大模型训练与微调 · 第 91 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《多语言模型 ⭐（北二外特色）》？…"},{"id":"lesson-092","number":92,"title":"多模态模型","moduleFolder":"C2_大模型训练与微调","moduleCode":"C2","moduleTitle":"大模型训练与微调","level":"三级/高级","color":"#238B9B","hours":2,"relativePath":"C2_大模型训练与微调/第92课_多模态模型.md","pptxPath":"pptx/C2_大模型训练与微调/第92课_多模态模型.pptx","headings":["多模态模型","从核心概念到训练师实战","为什么学习《多模态模型》？","学习目标","92.1 多模态模型的标注需求","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 11：任务单","考试实操 12：样本表设计","考试实操 13：小样本试跑","考试实操 14：操作步骤","考试实操 15：质量检查清单","考试实操 16：错例分析","考试实操 17：智慧图书馆应用","考试实操 18：评分细则","考试实操 19：提交包结构"],"summary":"多模态模型 从核心概念到训练师实战 大模型训练与微调 · 第 92 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《多模态模型》？…"},{"id":"lesson-093","number":93,"title":"LoRA 微调","moduleFolder":"C2_大模型训练与微调","moduleCode":"C2","moduleTitle":"大模型训练与微调","level":"三级/高级","color":"#238B9B","hours":2,"relativePath":"C2_大模型训练与微调/第93课_LoRA_微调.md","pptxPath":"pptx/C2_大模型训练与微调/第93课_LoRA_微调.pptx","headings":["LoRA 微调","从核心概念到训练师实战","为什么学习《LoRA 微调》？","学习目标","93.1 什么是 LoRA？","93.2 LoRA 微调实践","加载基座模型","配置 LoRA","Trainable params: 8,388,608 / 7,000,000,000 ≈ 0.1%","93.3 LoRA 训练脚本","准备训练数据（SFT 格式）","配置训练参数","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 13：任务单","考试实操 14：样本表设计"],"summary":"LoRA 微调 从核心概念到训练师实战 大模型训练与微调 · 第 93 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《LoRA 微调》？…"},{"id":"lesson-094","number":94,"title":"SFT数据准备","moduleFolder":"C2_大模型训练与微调","moduleCode":"C2","moduleTitle":"大模型训练与微调","level":"三级/高级","color":"#238B9B","hours":2,"relativePath":"C2_大模型训练与微调/第94课_SFT数据准备.md","pptxPath":"pptx/C2_大模型训练与微调/第94课_SFT数据准备.pptx","headings":["第 94 课  SFT数据准备","C2_大模型训练与微调 · 三级/高级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：大模型训练与应用：RAG、SFT与LoRA","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 94 课 SFT数据准备 C2大模型训练与微调 · 三级/高级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 SFT数据准备，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流程…"},{"id":"lesson-095","number":95,"title":"DPO与RLHF偏好数据构建","moduleFolder":"C2_大模型训练与微调","moduleCode":"C2","moduleTitle":"大模型训练与微调","level":"三级/高级","color":"#238B9B","hours":2,"relativePath":"C2_大模型训练与微调/第95课_DPO与RLHF偏好数据构建.md","pptxPath":"pptx/C2_大模型训练与微调/第95课_DPO与RLHF偏好数据构建.pptx","headings":["第 95 课  DPO与RLHF偏好数据构建","C2_大模型训练与微调 · 三级/高级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：大模型训练与应用：RAG、SFT与LoRA","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 95 课 DPO与RLHF偏好数据构建 C2大模型训练与微调 · 三级/高级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 DPO与RLHF偏好数据构建，解决真实项目中的三个问题： 1. 概…"},{"id":"lesson-096","number":96,"title":"大模型评估","moduleFolder":"C2_大模型训练与微调","moduleCode":"C2","moduleTitle":"大模型训练与微调","level":"三级/高级","color":"#238B9B","hours":2,"relativePath":"C2_大模型训练与微调/第96课_大模型评估.md","pptxPath":"pptx/C2_大模型训练与微调/第96课_大模型评估.pptx","headings":["大模型评估","从核心概念到训练师实战","为什么学习《大模型评估》？","学习目标","96.1 大模型评估体系","96.2 人工评估流程","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 12：任务单","考试实操 13：样本表设计","考试实操 14：小样本试跑","考试实操 15：操作步骤","考试实操 16：质量检查清单","考试实操 17：错例分析","考试实操 18：智慧图书馆应用","考试实操 19：评分细则"],"summary":"大模型评估 从核心概念到训练师实战 大模型训练与微调 · 第 96 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《大模型评估》？…"},{"id":"lesson-097","number":97,"title":"RAG数据构建与评估","moduleFolder":"C2_大模型训练与微调","moduleCode":"C2","moduleTitle":"大模型训练与微调","level":"三级/高级","color":"#238B9B","hours":2,"relativePath":"C2_大模型训练与微调/第97课_RAG数据构建与评估.md","pptxPath":"pptx/C2_大模型训练与微调/第97课_RAG数据构建与评估.pptx","headings":["第 97 课  RAG数据构建与评估","C2_大模型训练与微调 · 三级/高级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：大模型训练与应用：RAG、SFT与LoRA","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 97 课 RAG数据构建与评估 C2大模型训练与微调 · 三级/高级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 RAG数据构建与评估，解决真实项目中的三个问题： 1. 概念边界不清； 2…"},{"id":"lesson-098","number":98,"title":"综合实训","moduleFolder":"C2_大模型训练与微调","moduleCode":"C2","moduleTitle":"大模型训练与微调","level":"三级/高级","color":"#238B9B","hours":2,"relativePath":"C2_大模型训练与微调/第98课_综合实训.md","pptxPath":"pptx/C2_大模型训练与微调/第98课_综合实训.pptx","headings":["综合实训","从核心概念到训练师实战","为什么学习《综合实训》？","学习目标","98.1 实训任务：LoRA 微调完整流程","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 11：任务单","考试实操 12：样本表设计","考试实操 13：小样本试跑","考试实操 14：操作步骤","考试实操 15：质量检查清单","考试实操 16：错例分析","考试实操 17：智慧图书馆应用","考试实操 18：评分细则","考试实操 19：提交包结构"],"summary":"综合实训 从核心概念到训练师实战 大模型训练与微调 · 第 98 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《综合实训》？…"},{"id":"lesson-099","number":99,"title":"大模型部署基础","moduleFolder":"C2_大模型训练与微调","moduleCode":"C2","moduleTitle":"大模型训练与微调","level":"三级/高级","color":"#238B9B","hours":2,"relativePath":"C2_大模型训练与微调/第99课_大模型部署基础.md","pptxPath":"pptx/C2_大模型训练与微调/第99课_大模型部署基础.pptx","headings":["大模型部署基础","从核心概念到训练师实战","为什么学习《大模型部署基础》？","学习目标","99.1 模型部署方案","Ollama 一键部署","VLLM 高性能部署","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 11：任务单","考试实操 12：样本表设计","考试实操 13：小样本试跑","考试实操 14：操作步骤","考试实操 15：质量检查清单","考试实操 16：错例分析","考试实操 17：智慧图书馆应用"],"summary":"大模型部署基础 从核心概念到训练师实战 大模型训练与微调 · 第 99 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《大模型部署基础》？…"},{"id":"lesson-100","number":100,"title":"大模型部署综合实训","moduleFolder":"C2_大模型训练与微调","moduleCode":"C2","moduleTitle":"大模型训练与微调","level":"三级/高级","color":"#238B9B","hours":2,"relativePath":"C2_大模型训练与微调/第100课_大模型部署综合实训.md","pptxPath":"pptx/C2_大模型训练与微调/第100课_大模型部署综合实训.pptx","headings":["第 100 课  大模型部署综合实训","C2_大模型训练与微调 · 三级/高级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：大模型训练与应用：RAG、SFT与LoRA","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 100 课 大模型部署综合实训 C2大模型训练与微调 · 三级/高级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 大模型部署综合实训，解决真实项目中的三个问题： 1. 概念边界不清； 2.…"},{"id":"lesson-101","number":101,"title":"项目全流程管理","moduleFolder":"C3_项目管理与培训","moduleCode":"C3","moduleTitle":"项目管理与培训","level":"三级/高级","color":"#F2994A","hours":2,"relativePath":"C3_项目管理与培训/第101课_项目全流程管理.md","pptxPath":"pptx/C3_项目管理与培训/第101课_项目全流程管理.pptx","headings":["项目全流程管理","从核心概念到训练师实战","为什么学习《项目全流程管理》？","学习目标","101.1 标注项目的五个阶段","101.2 项目沟通管理","标注项目周报 - W3","进度","质量","问题","下周计划","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 12：任务单","考试实操 13：样本表设计","考试实操 14：小样本试跑"],"summary":"项目全流程管理 从核心概念到训练师实战 项目管理与培训 · 第 101 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《项目全流程管理》？…"},{"id":"lesson-102","number":102,"title":"成本估算与报价","moduleFolder":"C3_项目管理与培训","moduleCode":"C3","moduleTitle":"项目管理与培训","level":"三级/高级","color":"#F2994A","hours":2,"relativePath":"C3_项目管理与培训/第102课_成本估算与报价.md","pptxPath":"pptx/C3_项目管理与培训/第102课_成本估算与报价.pptx","headings":["成本估算与报价","从核心概念到训练师实战","为什么学习《成本估算与报价》？","学习目标","102.1 标注成本构成","102.2 定价参考","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 12：任务单","考试实操 13：样本表设计","考试实操 14：小样本试跑","考试实操 15：操作步骤","考试实操 16：质量检查清单","考试实操 17：错例分析","考试实操 18：智慧图书馆应用","考试实操 19：评分细则"],"summary":"成本估算与报价 从核心概念到训练师实战 项目管理与培训 · 第 102 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《成本估算与报价》？…"},{"id":"lesson-103","number":103,"title":"标注团队组建与培训","moduleFolder":"C3_项目管理与培训","moduleCode":"C3","moduleTitle":"项目管理与培训","level":"三级/高级","color":"#F2994A","hours":2,"relativePath":"C3_项目管理与培训/第103课_标注团队组建与培训.md","pptxPath":"pptx/C3_项目管理与培训/第103课_标注团队组建与培训.pptx","headings":["标注团队组建与培训","从核心概念到训练师实战","为什么学习《标注团队组建与培训》？","学习目标","103.1 标注员的招募标准","103.2 培训体系","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 12：任务单","考试实操 13：样本表设计","考试实操 14：小样本试跑","考试实操 15：操作步骤","考试实操 16：质量检查清单","考试实操 17：错例分析","考试实操 18：智慧图书馆应用","考试实操 19：评分细则"],"summary":"标注团队组建与培训 从核心概念到训练师实战 项目管理与培训 · 第 103 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《标注团队组建与培训》？…"},{"id":"lesson-104","number":104,"title":"培训课程设计","moduleFolder":"C3_项目管理与培训","moduleCode":"C3","moduleTitle":"项目管理与培训","level":"三级/高级","color":"#F2994A","hours":2,"relativePath":"C3_项目管理与培训/第104课_培训课程设计.md","pptxPath":"pptx/C3_项目管理与培训/第104课_培训课程设计.pptx","headings":["培训课程设计","从核心概念到训练师实战","为什么学习《培训课程设计》？","学习目标","104.1 训练师培训能力框架","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 11：任务单","考试实操 12：样本表设计","考试实操 13：小样本试跑","考试实操 14：操作步骤","考试实操 15：质量检查清单","考试实操 16：错例分析","考试实操 17：智慧图书馆应用","考试实操 18：评分细则","考试实操 19：提交包结构"],"summary":"培训课程设计 从核心概念到训练师实战 项目管理与培训 · 第 104 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《培训课程设计》？…"},{"id":"lesson-105","number":105,"title":"质量标准体系与KPI","moduleFolder":"C3_项目管理与培训","moduleCode":"C3","moduleTitle":"项目管理与培训","level":"三级/高级","color":"#F2994A","hours":2,"relativePath":"C3_项目管理与培训/第105课_质量标准体系与KPI.md","pptxPath":"pptx/C3_项目管理与培训/第105课_质量标准体系与KPI.pptx","headings":["第 105 课  质量标准体系与KPI","C3_项目管理与培训 · 三级/高级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：项目管理与职业发展：从个人技能到团队交付","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 105 课 质量标准体系与KPI C3项目管理与培训 · 三级/高级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 质量标准体系与KPI，解决真实项目中的三个问题： 1. 概念边界不清； 2…"},{"id":"lesson-106","number":106,"title":"质量审计与PDCA持续改进","moduleFolder":"C3_项目管理与培训","moduleCode":"C3","moduleTitle":"项目管理与培训","level":"三级/高级","color":"#F2994A","hours":2,"relativePath":"C3_项目管理与培训/第106课_质量审计与PDCA持续改进.md","pptxPath":"pptx/C3_项目管理与培训/第106课_质量审计与PDCA持续改进.pptx","headings":["第 106 课  质量审计与PDCA持续改进","C3_项目管理与培训 · 三级/高级工","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：项目管理与职业发展：从个人技能到团队交付","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 106 课 质量审计与PDCA持续改进 C3项目管理与培训 · 三级/高级工 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 质量审计与PDCA持续改进，解决真实项目中的三个问题： 1. 概念边…"},{"id":"lesson-107","number":107,"title":"客户沟通与需求分析","moduleFolder":"C3_项目管理与培训","moduleCode":"C3","moduleTitle":"项目管理与培训","level":"三级/高级","color":"#F2994A","hours":2,"relativePath":"C3_项目管理与培训/第107课_客户沟通与需求分析.md","pptxPath":"pptx/C3_项目管理与培训/第107课_客户沟通与需求分析.pptx","headings":["客户沟通与需求分析","从核心概念到训练师实战","为什么学习《客户沟通与需求分析》？","学习目标","107.1 需求分析八问","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 11：任务单","考试实操 12：样本表设计","考试实操 13：小样本试跑","考试实操 14：操作步骤","考试实操 15：质量检查清单","考试实操 16：错例分析","考试实操 17：智慧图书馆应用","考试实操 18：评分细则","考试实操 19：提交包结构"],"summary":"客户沟通与需求分析 从核心概念到训练师实战 项目管理与培训 · 第 107 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《客户沟通与需求分析》？…"},{"id":"lesson-108","number":108,"title":"案例分析 智慧图书馆项目","moduleFolder":"C3_项目管理与培训","moduleCode":"C3","moduleTitle":"项目管理与培训","level":"三级/高级","color":"#F2994A","hours":2,"relativePath":"C3_项目管理与培训/第108课_案例分析_智慧图书馆项目.md","pptxPath":"pptx/C3_项目管理与培训/第108课_案例分析_智慧图书馆项目.pptx","headings":["案例分析——智慧图书馆项目","从核心概念到训练师实战","为什么学习《案例分析——智慧图书馆项目》？","学习目标","108.1 项目背景","108.2 项目复盘","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 12：任务单","考试实操 13：样本表设计","考试实操 14：小样本试跑","考试实操 15：操作步骤","考试实操 16：质量检查清单","考试实操 17：错例分析","考试实操 18：智慧图书馆应用","考试实操 19：评分细则"],"summary":"案例分析——智慧图书馆项目 从核心概念到训练师实战 项目管理与培训 · 第 108 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《案例分析——智慧图书馆项目》？…"},{"id":"lesson-109","number":109,"title":"职业发展路径","moduleFolder":"C3_项目管理与培训","moduleCode":"C3","moduleTitle":"项目管理与培训","level":"三级/高级","color":"#F2994A","hours":2,"relativePath":"C3_项目管理与培训/第109课_职业发展路径.md","pptxPath":"pptx/C3_项目管理与培训/第109课_职业发展路径.pptx","headings":["职业发展路径","从核心概念到训练师实战","为什么学习《职业发展路径》？","学习目标","109.1 二级／一级训练师","109.2 转型方向","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 12：任务单","考试实操 13：样本表设计","考试实操 14：小样本试跑","考试实操 15：操作步骤","考试实操 16：质量检查清单","考试实操 17：错例分析","考试实操 18：智慧图书馆应用","考试实操 19：评分细则"],"summary":"职业发展路径 从核心概念到训练师实战 项目管理与培训 · 第 109 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《职业发展路径》？…"},{"id":"lesson-110","number":110,"title":"新技术趋势","moduleFolder":"C3_项目管理与培训","moduleCode":"C3","moduleTitle":"项目管理与培训","level":"三级/高级","color":"#F2994A","hours":2,"relativePath":"C3_项目管理与培训/第110课_新技术趋势.md","pptxPath":"pptx/C3_项目管理与培训/第110课_新技术趋势.pptx","headings":["新技术趋势","从核心概念到训练师实战","为什么学习《新技术趋势》？","学习目标","110.1 AI Agent","110.2 具身智能","三级考核准备","三级考核内容","120 课时总复习","课程全景回顾","核心技能自检","🎓 恭喜完成全部 120 课时培训！","下一步行动","动手实践","案例讨论","随堂测验","常见错误","本节课总结","课后作业","考试实操 12：任务单"],"summary":"新技术趋势 从核心概念到训练师实战 项目管理与培训 · 第 110 课 面向 AI 训练师 / 数据标注 / 智慧图书馆应用 理解概念 · 掌握流程 · 连接岗位 为什么学习《新技术趋势》？…"},{"id":"lesson-111","number":111,"title":"五级知识复盘与错题讲解","moduleFolder":"D_综合考核与复习","moduleCode":"D","moduleTitle":"综合考核与复习","level":"综合考核","color":"#EB5757","hours":2,"relativePath":"D_综合考核与复习/第111课_五级知识复盘与错题讲解.md","pptxPath":"pptx/D_综合考核与复习/第111课_五级知识复盘与错题讲解.pptx","headings":["第 111 课  五级知识复盘与错题讲解","D_综合考核与复习 · 五至三级贯通","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：综合复盘与考核：形成可展示的学习档案","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 111 课 五级知识复盘与错题讲解 D综合考核与复习 · 五至三级贯通 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 五级知识复盘与错题讲解，解决真实项目中的三个问题： 1. 概念边界不清； …"},{"id":"lesson-112","number":112,"title":"四级Python与模型训练复盘","moduleFolder":"D_综合考核与复习","moduleCode":"D","moduleTitle":"综合考核与复习","level":"综合考核","color":"#EB5757","hours":2,"relativePath":"D_综合考核与复习/第112课_四级Python与模型训练复盘.md","pptxPath":"pptx/D_综合考核与复习/第112课_四级Python与模型训练复盘.pptx","headings":["第 112 课  四级Python与模型训练复盘","D_综合考核与复习 · 五至三级贯通","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：综合复盘与考核：形成可展示的学习档案","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 112 课 四级Python与模型训练复盘 D综合考核与复习 · 五至三级贯通 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 四级Python与模型训练复盘，解决真实项目中的三个问题： 1. …"},{"id":"lesson-113","number":113,"title":"三级大模型与项目管理复盘","moduleFolder":"D_综合考核与复习","moduleCode":"D","moduleTitle":"综合考核与复习","level":"综合考核","color":"#EB5757","hours":2,"relativePath":"D_综合考核与复习/第113课_三级大模型与项目管理复盘.md","pptxPath":"pptx/D_综合考核与复习/第113课_三级大模型与项目管理复盘.pptx","headings":["第 113 课  三级大模型与项目管理复盘","D_综合考核与复习 · 五至三级贯通","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：综合复盘与考核：形成可展示的学习档案","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 113 课 三级大模型与项目管理复盘 D综合考核与复习 · 五至三级贯通 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 三级大模型与项目管理复盘，解决真实项目中的三个问题： 1. 概念边界不清…"},{"id":"lesson-114","number":114,"title":"五级模拟理论考试","moduleFolder":"D_综合考核与复习","moduleCode":"D","moduleTitle":"综合考核与复习","level":"综合考核","color":"#EB5757","hours":2,"relativePath":"D_综合考核与复习/第114课_五级模拟理论考试.md","pptxPath":"pptx/D_综合考核与复习/第114课_五级模拟理论考试.pptx","headings":["第 114 课  五级模拟理论考试","D_综合考核与复习 · 五至三级贯通","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：综合复盘与考核：形成可展示的学习档案","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 114 课 五级模拟理论考试 D综合考核与复习 · 五至三级贯通 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 五级模拟理论考试，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流…"},{"id":"lesson-115","number":115,"title":"五级模拟实操考试","moduleFolder":"D_综合考核与复习","moduleCode":"D","moduleTitle":"综合考核与复习","level":"综合考核","color":"#EB5757","hours":2,"relativePath":"D_综合考核与复习/第115课_五级模拟实操考试.md","pptxPath":"pptx/D_综合考核与复习/第115课_五级模拟实操考试.pptx","headings":["第 115 课  五级模拟实操考试","D_综合考核与复习 · 五至三级贯通","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：综合复盘与考核：形成可展示的学习档案","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 115 课 五级模拟实操考试 D综合考核与复习 · 五至三级贯通 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 五级模拟实操考试，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流…"},{"id":"lesson-116","number":116,"title":"四级模拟理论与代码考试","moduleFolder":"D_综合考核与复习","moduleCode":"D","moduleTitle":"综合考核与复习","level":"综合考核","color":"#EB5757","hours":2,"relativePath":"D_综合考核与复习/第116课_四级模拟理论与代码考试.md","pptxPath":"pptx/D_综合考核与复习/第116课_四级模拟理论与代码考试.pptx","headings":["第 116 课  四级模拟理论与代码考试","D_综合考核与复习 · 五至三级贯通","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：综合复盘与考核：形成可展示的学习档案","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 116 课 四级模拟理论与代码考试 D综合考核与复习 · 五至三级贯通 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 四级模拟理论与代码考试，解决真实项目中的三个问题： 1. 概念边界不清； …"},{"id":"lesson-117","number":117,"title":"四级数据处理综合实操","moduleFolder":"D_综合考核与复习","moduleCode":"D","moduleTitle":"综合考核与复习","level":"综合考核","color":"#EB5757","hours":2,"relativePath":"D_综合考核与复习/第117课_四级数据处理综合实操.md","pptxPath":"pptx/D_综合考核与复习/第117课_四级数据处理综合实操.pptx","headings":["第 117 课  四级数据处理综合实操","D_综合考核与复习 · 五至三级贯通","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：综合复盘与考核：形成可展示的学习档案","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 117 课 四级数据处理综合实操 D综合考核与复习 · 五至三级贯通 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 四级数据处理综合实操，解决真实项目中的三个问题： 1. 概念边界不清； 2.…"},{"id":"lesson-118","number":118,"title":"三级案例分析模拟考试","moduleFolder":"D_综合考核与复习","moduleCode":"D","moduleTitle":"综合考核与复习","level":"综合考核","color":"#EB5757","hours":2,"relativePath":"D_综合考核与复习/第118课_三级案例分析模拟考试.md","pptxPath":"pptx/D_综合考核与复习/第118课_三级案例分析模拟考试.pptx","headings":["第 118 课  三级案例分析模拟考试","D_综合考核与复习 · 五至三级贯通","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：综合复盘与考核：形成可展示的学习档案","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 118 课 三级案例分析模拟考试 D综合考核与复习 · 五至三级贯通 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 三级案例分析模拟考试，解决真实项目中的三个问题： 1. 概念边界不清； 2.…"},{"id":"lesson-119","number":119,"title":"三级项目答辩彩排","moduleFolder":"D_综合考核与复习","moduleCode":"D","moduleTitle":"综合考核与复习","level":"综合考核","color":"#EB5757","hours":2,"relativePath":"D_综合考核与复习/第119课_三级项目答辩彩排.md","pptxPath":"pptx/D_综合考核与复习/第119课_三级项目答辩彩排.pptx","headings":["第 119 课  三级项目答辩彩排","D_综合考核与复习 · 五至三级贯通","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：综合复盘与考核：形成可展示的学习档案","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 119 课 三级项目答辩彩排 D综合考核与复习 · 五至三级贯通 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 三级项目答辩彩排，解决真实项目中的三个问题： 1. 概念边界不清； 2. 操作流…"},{"id":"lesson-120","number":120,"title":"结业考核与学习档案归档","moduleFolder":"D_综合考核与复习","moduleCode":"D","moduleTitle":"综合考核与复习","level":"综合考核","color":"#EB5757","hours":2,"relativePath":"D_综合考核与复习/第120课_结业考核与学习档案归档.md","pptxPath":"pptx/D_综合考核与复习/第120课_结业考核与学习档案归档.pptx","headings":["第 120 课  结业考核与学习档案归档","D_综合考核与复习 · 五至三级贯通","1. 为什么学习本课？","2. 学习目标","3. 课前导入案例","4. 核心概念","5. 标准工作流程","6. 工具与方法","7. 动手实操","8. 北二外特色迁移","9. 随堂测验","10. 常见错误与纠正","11. 课后作业","12. 评分标准","大学生学习拓展：综合复盘与考核：形成可展示的学习档案","知识加餐：本课在AI项目中的位置","课堂实训升级：从“听懂”到“做出来”","工具链建议：国产模型 + 本地工具 + 可复现记录","课堂讨论与课后反思"],"summary":"第 120 课 结业考核与学习档案归档 D综合考核与复习 · 五至三级贯通 北二外海棠 AI 实验室 课时：2 学时 · 特色场景：智慧图书馆/多语种数据 1. 为什么学习本课？ AI 训练师的工作不是“点按钮”，而是把业务问题转化为可训练、可评估、可交付的数据和模型任务。 本课聚焦 结业考核与学习档案归档，解决真实项目中的三个问题： 1. 概念边界不清； 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