- 了解 AI 和机器学习概念
- 具有 AI 模型开发经验
- 基本编程技能（Python 推荐）
观众
- AI 开发人员
- 机器学习工程师
- 系统架构师
Optimizing AI Models for Edge Devices 专注于优化 AI 模型以在边缘硬件上高效运行的技术。本课程涵盖模型压缩、量化和其他优化技术，为边缘设备构建高性能 AI 模型提供实用知识。
这种由讲师指导的现场培训（在线或远程）面向希望优化 AI 模型以进行边缘部署的中级 AI 开发人员、机器学习工程师和系统架构师。
在培训结束时，参与者将能够：
- 了解在边缘设备上部署 AI 模型的挑战和要求。
- 应用模型压缩技术来减小 AI 模型的大小和复杂性。
- 利用量化方法提高边缘硬件上的模型效率。
- 实施修剪和其他优化技术以提高模型性能。
- 在各种边缘设备上部署优化的 AI 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。
边缘 AI 优化简介
- 边缘 AI 概述及其挑战
- 边缘设备模型优化的重要性
- 边缘应用中优化 AI 模型的案例研究
模型压缩技术
- 模型压缩简介
- 减小模型大小的技术
- 模型压缩的动手练习
量化方法
- 量化概述及其优势
- 量化类型（训练后、量化感知训练）
- 模型量化的动手练习
修剪和其他优化技术
- 修剪简介
- 修剪 AI 模型的方法
- 其他优化技术（例如，知识蒸馏）
- 模型修剪和优化的动手练习
在边缘设备上部署优化模型
- 准备边缘设备环境
- 部署和测试优化模型
- 排查部署问题
- 模型部署的动手练习
优化工具和框架
- 工具和框架概述（例如，TensorFlow Lite、ONNX）
- 使用 TensorFlow Lite 进行模型优化
- 使用优化工具进行动手练习
实际应用和案例研究
- 回顾成功的边缘 AI 优化项目
- 讨论行业特定用例
- 用于构建和优化实际应用程序的动手项目
摘要和后续步骤
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