- 对机器学习和深度学习概念有深入理解
- 具备使用AI框架（如PyTorch或TensorFlow）的经验
- 熟悉文本、图像和音频数据处理
受众
- AI开发者
- 机器学习工程师
- 研究人员
多模态AI整合了多种数据类型，如文本、图像和音频，以增强机器学习模型和应用。
本课程为讲师指导的线下或线上培训，面向希望使用开源框架构建自定义多模态AI模型的高级AI开发者、机器学习工程师和研究人员。
通过本培训，参与者将能够：
- 理解多模态学习和数据融合的基础知识。
- 使用DeepSeek、OpenAI、Hugging Face和PyTorch实现多模态模型。
- 优化和微调模型，以实现文本、图像和音频的集成。
- 在实际应用中部署多模态AI模型。
课程形式
- 互动式讲座和讨论。
- 大量练习和实践。
- 在实时实验室环境中进行动手实践。
课程定制选项
- 如需为本课程定制培训，请联系我们安排。
多模态AI简介
- 多模态AI概述及实际应用
- 整合文本、图像和音频数据的挑战
- 最新研究进展
数据处理与特征工程
- 处理文本、图像和音频数据集
- 多模态学习的预处理技术
- 特征提取与数据融合策略
使用PyTorch和Hugging Face构建多模态模型
- PyTorch在多模态学习中的介绍
- 使用Hugging Face Transformers进行NLP和视觉任务
- 在统一AI模型中结合不同模态
实现语音、视觉和文本融合
- 整合OpenAI Whisper进行语音识别
- 应用DeepSeek-Vision进行图像处理
- 跨模态学习的融合技术
训练与优化多模态AI模型
- 多模态AI的模型训练策略
- 优化技术与超参数调优
- 解决偏差并提高模型泛化能力
在实际应用中部署多模态AI
- 导出模型用于生产环境
- 在云平台上部署AI模型
- 性能监控与模型维护
高级主题与未来趋势
- 多模态AI中的零样本和少样本学习
- 伦理考量与负责任的AI开发
- 多模态AI研究的新兴趋势
总结与下一步
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