- 瞭解 AI 和機器學習概念
- 具有 AI 模型開發經驗
- 基本程式設計技能（Python 推薦）
觀眾
- AI 開發人員
- 機器學習工程師
- 系統架構師
Optimizing AI Models for Edge Devices 專注於優化 AI 模型以在邊緣硬體上高效運行的技術。本課程涵蓋模型壓縮、量化和其他優化技術，為邊緣設備構建高性能 AI 模型提供實用知識。
這種由講師指導的現場培訓（在線或遠端）面向希望優化 AI 模型以進行邊緣部署的中級 AI 開發人員、機器學習工程師和系統架構師。
在培訓結束時，參與者將能夠：
- 瞭解在邊緣設備上部署 AI 模型的挑戰和要求。
- 應用模型壓縮技術來減小 AI 模型的大小和複雜性。
- 利用量化方法提高邊緣硬體上的模型效率。
- 實施修剪和其他優化技術以提高模型性能。
- 在各種邊緣設備上部署優化的 AI 模型。
課程形式
- 互動講座和討論。
- 大量的練習和練習。
- 在現場實驗室環境中動手實施。
課程自定義選項
- 如需申請本課程的定製培訓，請聯繫我們進行安排。
邊緣 AI 優化簡介
- 邊緣 AI 概述及其挑戰
- 邊緣設備模型優化的重要性
- 邊緣應用中優化 AI 模型的案例研究
模型壓縮技術
- 模型壓縮簡介
- 減小模型大小的技術
- 模型壓縮的動手練習
量化方法
- 量化概述及其優勢
- 量化類型（訓練後、量化感知訓練）
- 模型量化的動手練習
修剪和其他優化技術
- 修剪簡介
- 修剪 AI 模型的方法
- 其他優化技術（例如，知識蒸餾）
- 模型修剪和優化的動手練習
在邊緣設備上部署優化模型
- 準備邊緣設備環境
- 部署和測試優化模型
- 排查部署問題
- 模型部署的動手練習
優化工具和框架
- 工具和框架概述（例如，TensorFlow Lite、ONNX）
- 使用 TensorFlow Lite 進行模型優化
- 使用優化工具進行動手練習
實際應用和案例研究
- 回顧成功的邊緣 AI 優化專案
- 討論行業特定用例
- 用於構建和優化實際應用程式的動手專案
摘要和後續步驟
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