- 對機器學習和深度學習概念有深入理解
- 具備使用AI框架（如PyTorch或TensorFlow）的經驗
- 熟悉文本、圖像和音頻數據處理
受衆
- AI開發者
- 機器學習工程師
- 研究人員
多模態AI整合了多種數據類型，如文本、圖像和音頻，以增強機器學習模型和應用。
本課程爲講師指導的線下或線上培訓，面向希望使用開源框架構建自定義多模態AI模型的高級AI開發者、機器學習工程師和研究人員。
通過本培訓，參與者將能夠：
- 理解多模態學習和數據融合的基礎知識。
- 使用DeepSeek、OpenAI、Hugging Face和PyTorch實現多模態模型。
- 優化和微調模型，以實現文本、圖像和音頻的集成。
- 在實際應用中部署多模態AI模型。
課程形式
- 互動式講座和討論。
- 大量練習和實踐。
- 在即時實驗室環境中進行動手實踐。
課程定製選項
- 如需爲本課程定製培訓，請聯繫我們安排。
多模態AI簡介
- 多模態AI概述及實際應用
- 整合文本、圖像和音頻數據的挑戰
- 最新研究進展
數據處理與特徵工程
- 處理文本、圖像和音頻數據集
- 多模態學習的預處理技術
- 特徵提取與數據融合策略
使用PyTorch和Hugging Face構建多模態模型
- PyTorch在多模態學習中的介紹
- 使用Hugging Face Transformers進行NLP和視覺任務
- 在統一AI模型中結合不同模態
實現語音、視覺和文本融合
- 整合OpenAI Whisper進行語音識別
- 應用DeepSeek-Vision進行圖像處理
- 跨模態學習的融合技術
訓練與優化多模態AI模型
- 多模態AI的模型訓練策略
- 優化技術與超參數調優
- 解決偏差並提高模型泛化能力
在實際應用中部署多模態AI
- 導出模型用於生產環境
- 在雲平臺上部署AI模型
- 性能監控與模型維護
高級主題與未來趨勢
- 多模態AI中的零樣本和少樣本學習
- 倫理考量與負責任的AI開發
- 多模態AI研究的新興趨勢
總結與下一步
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