- 對機器學習和深度學習概念有深入的了解
- 對PyTorch或TensorFlow等人工智慧框架有經驗
- 對文本、圖像和音頻數據處理有一定的了解
觀眾
- AI 開發人員
- 機器學習工程師
- 研究人員
Multimodal AI整合多種數據類型，如文本、圖像和音頻，以增強機器學習模型和應用程序。
這個由教師主導的在線培訓課程（在線或現場）旨在讓高級人工智能開發人員、機器學習工程師和研究人員了解PEG和使用開源框架構建自定義多模態人工智能模型。
通過這次培訓，參加者將能夠：
- 了解多模態學習和數據融合的基本原理。
- 使用DeepSeek、OpenAI、Hugging Face和PyTorch實現多模態模型。
- 優化和調整文本、圖像和音頻集成的模型。
- 在現實世界的應用中部署多模態AI模型。
課程格式
- 互動講座和討論。
- 大量的練習和實踐。
- 在實時實驗室環境中進行實操。
課程定制選項
- 如需請求此課程的定制培訓，請聯繫我們安排。
多模態AI及其應用概覽
- 文本、圖像和音頻數據集成的挑戰
- 最先進的研究和進展
- 多模態AI的應用概覽
數據處理和特徵工程
- 處理文本、圖像和音頻數據集
- 多模態學習的預處理技術
- 特徵提取和數據融合策略
使用Py及及結合不同模態的統一AI模型的Transformer進行NLP和計VISION任務
- Py的多模態學習介紹
- 使用Transformers進行NLP和計VISION任務
- 使用Py及和清華大學的Transformers進行NLP和計VISION任務
實現語音、視覺和文本融合
- 集成Whisper進行語音識別
- 應用Deep進行圖像處理
- 交叉模態學習的融合技術
訓練和優化模型
- 多模態AI的模型訓練策略
- 優化技術和超參數調整
- 處理偏見和提高模型泛化能力
在實際應用中部署模型
- 導出模型以供生產使用
- 在雲平台上部署AI模型
- 性能監控和模型維護
總結和結論
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