- 对机器学习和深度学习概念有深入的了解
- 对PyTorch或TensorFlow等人工智慧框架有经验
- 对文本、图像和音频数据处理有一定的了解
观众
- AI 开发人员
- 机器学习工程师
- 研究人员
Multimodal AI整合多种数据类型，如文本、图像和音频，以增强机器学习模型和应用程序。
这个由教师主导的在线培训课程（在线或现场）旨在让高级人工智能开发人员、机器学习工程师和研究人员了解PEG和使用开源框架构建自定义多模态人工智能模型。
通过这次培训，参加者将能够：
- 了解多模态学习和数据融合的基本原理。
- 使用DeepSeek、OpenAI、Hugging Face和PyTorch实现多模态模型。
- 优化和调整文本、图像和音频集成的模型。
- 在现实世界的应用中部署多模态AI模型。
课程格式
- 互动讲座和讨论。
- 大量的练习和实践。
- 在实时实验室环境中进行实操。
课程定制选项
- 如需请求此课程的定制培训，请联系我们安排。
多模态AI及其应用概览
- 文本、图像和音频数据集成的挑战
- 最先进的研究和进展
- 多模态AI的应用概览
数据处理和特征工程
- 处理文本、图像和音频数据集
- 多模态学习的预处理技术
- 特征提取和数据融合策略
使用Py及及结合不同模态的统一AI模型的Transformer进行NLP和计VISION任务
- Py的多模态学习介绍
- 使用Transformers进行NLP和计VISION任务
- 使用Py及和清华大学的Transformers进行NLP和计VISION任务
实现语音、视觉和文本融合
- 集成Whisper进行语音识别
- 应用Deep进行图像处理
- 交叉模态学习的融合技术
训练和优化模型
- 多模态AI的模型训练策略
- 优化技术和超参数调整
- 处理偏见和提高模型泛化能力
在实际应用中部署模型
- 导出模型以供生产使用
- 在云平台上部署AI模型
- 性能监控和模型维护
总结和结论
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