- 具备机器学习基础知识
- 具备Python和深度学习框架的经验
- 熟悉嵌入式系统或边缘设备的限制
受众
- 嵌入式AI开发者
- 边缘计算专家
- 专注于边缘部署的机器学习工程师
模型微调是将预训练模型适配到特定任务或环境的过程。
本次由讲师指导的培训（线上或线下）面向中级嵌入式AI开发人员和边缘计算专家，旨在微调和优化轻量级AI模型，以便在资源受限的设备上部署。
培训结束后，学员将能够：
- 选择并适配适合边缘部署的预训练模型。
- 应用量化、剪枝和其他压缩技术，以减少模型大小和延迟。
- 使用迁移学习微调模型，以提升任务特定性能。
- 在真实的边缘硬件平台上部署优化后的模型。
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动手操作。
课程定制选项
- 如需为本课程定制培训，请联系我们安排。
Edge AI 和模型优化简介
- 了解边缘计算和AI工作负载
- 权衡：性能与资源限制
- 模型优化策略概述
模型选择与预训练
- 选择轻量级模型（如MobileNet、TinyML、SqueezeNet）
- 了解适合边缘设备的模型架构
- 使用预训练模型作为基础
Fine-Tuning 与迁移学习
- 迁移学习原理
- 将模型适配到自定义数据集
- 实际微调工作流程
模型量化
- 训练后量化技术
- 量化感知训练
- 评估与权衡
模型剪枝与压缩
- 剪枝策略（结构化与非结构化）
- 压缩与权重共享
- 压缩模型基准测试
部署框架与工具
- TensorFlow Lite、PyTorch Mobile、ONNX
- 边缘硬件兼容性与运行时环境
- 跨平台部署工具链
实际部署
- 部署到Raspberry Pi、Jetson Nano和移动设备
- 性能分析与基准测试
- 解决部署问题
总结与下一步
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