- 对人工智能和机器学习有基本的了解
- 熟练使用 Python 编程
- 熟悉数据处理和预处理
观众
- 人工智能研究人员
- 数据科学家
- 机器学习工程师
多模态人工智能是一个创新领域，它结合了来自各种感官输入的信息，以创建以更像人类的方式理解世界并与之互动的系统。
这种由讲师指导的现场培训（在线或现场）面向希望创建能够处理和解释多模态数据的智能系统的中级人工智能研究人员、数据科学家和机器学习工程师。
在培训结束时，参与者将能够：
- 了解多模态 AI 及其应用的原理。
- 实施数据融合技术以组合不同类型的数据。
- 构建和训练可以处理视觉、文本和听觉信息的模型。
- 评估多模态 AI 系统的性能。
- 解决与多模态数据相关的道德和隐私问题。
课程形式
- 互动讲座和讨论。
- 大量的练习和练习。
- 在现场实验室环境中动手实施。
课程自定义选项
- 如需申请本课程的定制培训，请联系我们进行安排。
多模态 AI 简介
- 了解多模态数据
- 关键概念和定义
- 多模态学习的历史和演变
多模态数据处理
- 数据收集和预处理
- 从不同模式中提取特征
- 数据融合技术
多模态表示学习
- 学习联合表示
- 跨模态嵌入
- 跨模式的迁移学习
多模态对齐和翻译
- 对齐来自多种模态的数据
- 跨模态检索系统
- 模态之间的翻译（例如，文本到图像、图像到文本）
多模态推理与推理
- 使用多模态数据进行逻辑和推理
- 多模态 AI 中的推理技术
- 在问答和决策中的应用
多模态 AI 中的生成模型
- 用于多模态数据的生成对抗网络 （GAN）
- 用于跨模态生成的变分自动编码器 （VAE）
- 生成式多模态人工智能的创造性应用
多模态融合技术
- 早期、晚期和混合融合方法
- 多模态融合中的注意力机制
- 融合，实现强大的感知和交互
多模态AI的应用
- 多模态人机交互
- 自动驾驶汽车中的人工智能
- 医疗保健应用（例如，医学成像和诊断）
伦理考虑与挑战
- 多式联运系统中的偏差和公平性
- 多模态数据的隐私问题
- 多模态人工智能系统的道德设计和部署
多模态 AI 中的高级主题
- 多模态变压器
- 多模态人工智能中的自监督学习
- 多模态机器学习的未来
摘要和后续步骤
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