- 了解机器学习基础知识
- Python 个程式设计经验
- 熟悉预训练模型及其应用
观众
- 数据科学家
- 机器学习工程师
- AI 研究人员
微调模型和 LLM 是使预先训练的机器学习模型适应特定任务和数据集的关键过程。本课程探讨了微调的技术、工具和最佳实践，重点介绍实现高性能的实际实施和优化策略。
这种由讲师指导的实时培训（在线或现场）面向希望为特定任务和数据集自定义预训练模型的中高级专业人员。
在本次培训结束时，参与者将能够：
- 了解微调的原理及其应用。
- 准备数据集以微调预训练模型。
- 为 NLP 任务微调大型语言模型 （LLM）。
- 优化模型性能并解决常见挑战。
课程形式
- 互动讲座和讨论。
- 大量的练习和练习。
- 在即时实验室环境中动手实施。
课程自定义选项
- 要申请本课程的定制培训，请联系我们进行安排。
微调简介
- 什么是微调？
- 微调的使用案例和优势
- 预训练模型和迁移学习概述
准备微调
- 收集和清理数据集
- 了解特定于任务的数据要求
- 探索性数据分析和预处理
微调技术
- 迁移学习和特征提取
- 带 Hugging Face 的微调变压器
- 针对监督任务与无监督任务进行微调
微调 Large Language Models (LLMs)
- 使 LLM 适应 NLP 任务（例如，文字分类、摘要）
- 使用自订数据集训练 LLM
- 使用提示工程控制 LLM 行为
优化和评估
- 超参数优化
- 评估模型性能
- 解决过拟合和欠拟合
扩展微调工作
- 在分散式系统上进行微调
- 利用基于云端解决方案实现可扩充性
- 案例研究：大规模微调专案
最佳实践和挑战
- 微调成功的最佳实践
- 常见挑战和故障排除
- 微调 AI 模型的道德注意事项
进阶主题（选择）
- 微调多模态模型
- 零样本和少数样本学习
- 探索LoRA （Low-Rank Adaptation） 技术
总结和后续步骤
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