- 对机器学习概念的基本了解
- 熟悉 Python 编程
- 神经网络和深度学习知识
观众
- 数据科学家
- 软件开发人员
- AI爱好者
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。
这种由讲师指导的现场培训（在线或远程）面向希望在各种应用程序中实施和利用小型语言模型的初级到中级数据科学家和开发人员。
在培训结束时，参与者将能够：
- 了解小型语言模型的体系结构和功能。
- 为文本生成和情绪分析等任务实施 SLM。
- 针对特定用例优化和微调 SLM。
- 在资源受限的环境中部署 SLM。
- 评估和解释 SLM 在实际场景中的性能。
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。
小型语言模型 （SLM） 简介
- 语言模型概述
- 从大型语言模型到小型语言模型的演变
- SLM的体系结构和设计
- SLM的优点和局限性
技术基础
- 了解神经网络和参数
- SLM的培训过程
- 数据需求和模型优化
- 语言模型的评估指标
自然语言处理中的 SLM
- 使用 SLM 生成文本
- 语言翻译和本地化
- 情感分析和文本分类
- 问答和聊天机器人
SLM 的实际应用
- 移动应用程序：设备端语言处理
- 嵌入式系统：物联网设备中的 SLM
- 隐私保护 AI：本地数据处理
- 边缘计算：低延迟环境中的 SLM
案例研究
- 分析SLM的成功部署
- 行业特定应用（医疗保健、Finance 等）
- 比较研究：SLM 与生产中的大型模型
未来方向
- SLM的研究趋势
- 扩展和部署方面的挑战
- 道德考量和负责任的人工智能
- 未来之路：下一代SLM
实践研讨会
- 构建用于文本生成的简单 SLM
- 将 SLM 集成到移动应用程序中
- 针对特定任务微调 SLM
- 性能分析和模型可解释性
顶点项目
- 确定 SLM 应用程序的问题空间
- 设计和实施 SLM 解决方案
- 测试和迭代模型
- 介绍项目和成果
摘要和后续步骤
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