- 精通 Python 个程式设计
- 了解深度学习概念
- 使用微调预训练模型的经验
观众
- AI 研究人员
- 数据科学家
- 机器学习从业者
Fine-Tuning Multimodal Models 重点介绍用于调整处理多种数据类型（如文本、图像和视频）的模型的高级技术。参与者将深入了解如何处理复杂数据集、优化模型性能以及将这些模型部署到实际应用，例如视觉问答和内容生成。
这种由讲师指导的现场培训（在线或现场）面向希望掌握创新 AI 解决方案的多模态模型微调的高级专业人员。
在本次培训结束时，参与者将能够：
- 了解 CLIP 和 Flamingo 等多模态模型的架构。
- 有效地准备和预处理多模态数据集。
- 针对特定任务微调多模态模型。
- 针对实际应用程式和性能优化模型。
课程形式
- 互动讲座和讨论。
- 大量的练习和练习。
- 在即时实验室环境中动手实施。
课程自定义选项
- 要申请本课程的定制培训，请联系我们进行安排。
多模态模型简介
- 多模态机器学习概述
- 多模态模型的应用
- 处理多种数据类型的挑战
多模态模型的架构
- 探索 CLIP、Flamingo 和 BLIP 等模型
- 了解跨模态注意力机制
- 可扩展性和效率的架构注意事项
准备多模态数据集
- 数据收集和注释技术
- 预处理文字、图像和视频输入
- 平衡多模态任务的数据集
多模态模型的微调技术
- 为多模态模型设置训练管道
- 管理记忆体和计算约束
- 处理模态之间的一致性
微调多模态模型的应用
- 视觉问答
- 图像和视频字幕
- 使用多模态输入生成内容
性能优化和评估
- 多模式任务的评估指标
- 优化生产的延迟和输送量
- 确保不同模式的稳健性和一致性
部署多模态模型
- 打包模型以进行部署
- Scala云平台上的 ble 推理
- 即时应用程式和整合
案例研究和动手实验
- 微调 CLIP 以实现基于内容的图像检索
- 使用文字和视频训练多模式聊天机器人
- 实施跨模式检索系统
总结和后续步骤
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