提示工程与少样本微调 培训
提示工程与少样本微调课程为学员提供了使用提示工程技术和少样本学习来有效指导大型语言模型（LLMs）的实用知识。课程强调在不进行大量微调的情况下实现最佳效果，使预训练模型能够高效适应各种任务。
本课程为讲师指导的实时培训（线上或线下），面向希望利用提示工程和少样本学习来优化LLM性能的中级专业人士。
培训结束后，学员将能够：
- 理解提示工程和少样本学习的原理。
- 为各种NLP任务设计有效的提示。
- 利用少样本技术以最少的数据适应LLMs。
- 为实际应用优化LLM性能。
课程形式
- 互动讲座与讨论。
- 大量练习与实践。
- 在实时实验室环境中进行实际操作。
课程定制选项
- 如需定制本课程，请联系我们安排。
感谢您发送咨询！我们的团队成员将很快与您联系。
感谢您发送预订！我们的团队成员将很快与您联系。
课程大纲
提示工程简介
- 什么是提示工程？
- 提示设计在LLMs中的重要性
- 零样本、单样本与少样本方法的比较
设计有效提示
- 高质量提示的构建原则
- 尝试不同的提示变体
- 提示设计中的常见挑战
少样本微调
- 少样本学习概述
- 在任务特定LLM适应中的应用
- 将少样本示例整合到提示中
提示工程工具实践
- 使用OpenAI API进行提示实验
- 探索Hugging Face Transformers的提示设计
- 评估提示变体的影响
优化LLM性能
- 评估输出并优化提示
- 整合上下文以获得更好的结果
- 处理LLM响应中的歧义和偏见
提示工程的应用
- 文本生成与摘要
- 情感分析与分类
- 创意写作与代码生成
部署基于提示的解决方案
- 将提示整合到应用中
- 监控性能与可扩展性
- 案例研究与实际示例
总结与下一步
要求
- 具备自然语言处理（NLP）的基础知识
- 熟悉Python编程
- 有大型语言模型（LLMs）经验者优先
受众
- AI开发者
- NLP工程师
- 机器学习从业者
14 小时
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提示工程与少样本微调 培训 - 询价
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