- 对机器学习概念的基本了解
- 熟悉 Python 编程
- 自然语言处理基础知识
观众
- 数据科学家
- 机器学习工程师
小型语言模型 （SLM） 是 AI 的一个尖端子集，可在计算资源有限的设备上实现高效的语言处理。
这种以讲师为主导的现场培训（在线或远程）面向中级数据科学家和机器学习工程师，他们希望创建和应用为特定领域（如法律、医学和技术领域）量身定制的小型语言模型。
在培训结束时，参与者将能够：
- 了解特定领域语言模型的重要性和应用。
- 整理和预处理用于模型训练的专用数据集。
- 为特定于领域的应用程序训练和微调语言模型。
- 使用领域相关指标对模型进行评估和基准测试。
- 在实际场景中部署特定于领域的语言模型。
课程形式
- 互动讲座和讨论。
- 大量的练习和练习。
- 在现场实验室环境中动手实施。
课程自定义选项
- 如需申请本课程的定制培训，请联系我们进行安排。
领域特定语言模型简介
- AI 中的语言模型概述
- 语言模型专业化的重要性
- 成功领域特定模型的案例研究
数据管理和预处理
- 识别和收集特定领域的数据集
- 数据清理和预处理技术
- 数据集创建中的道德考量
模型训练和微调
- 迁移学习和微调简介
- 为特定领域的训练选择基本模型
- 有效微调的技术
评估指标和模型性能
- 特定领域模型评估的指标
- 针对特定领域任务对模型进行基准测试
- 了解局限性和权衡
部署策略
- 将语言模型集成到特定领域的应用程序中
- Scala部署模型的可及性和维护
- 部署中的持续学习和模型更新
法律领域焦点
- 法律语言模型的特殊注意事项
- 培训的判例法和成文法语料库
- 在法律研究和文件分析中的应用
医疗领域重点
- 医学语言处理的挑战
- HIPAA 合规性和数据隐私
- 医学文献综述和患者互动中的用例
技术领域重点
- 技术术语及其对语言模型的影响
- Collaboration 与主题专家
- 技术文档生成和代码注释
项目与评估
- 项目提案和初始数据集收集
- 展示已完成的项目和模型性能
- 最终评估和反馈
摘要和后续步骤
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