Introduction
Multimodal AI combines information from text, images, audio, and other data types to create systems with richer understanding. This course introduces core multimodal architectures, fusion techniques, and training strategies. Participants will learn how models like CLIP and vision-language transformers integrate multiple modalities. Through hands-on exercises, learners will build small multimodal applications. By the end, attendees will be prepared to explore advanced multimodal AI research.
Course Objectives
- Understand multimodal learning concepts
- Explore fusion techniques for combining data types
- Learn key multimodal model architectures
- Apply multimodal frameworks to practical tasks
- Study real-world multimodal applications
Target Audience
- Deep learning practitioners
- ML researchers
- Vision and NLP developers
- Students in advanced AI
- Innovation teams building multimodal products
Course Outline
- 5 Sections
- 0 Lessons
- 5 Days
Expand all sectionsCollapse all sections
- Day 1: Multimodal Basics• Modality types
• Early vs. late fusion
• Embedding spaces
• Alignment challenges
• Hands-on: Multimodal dataset exploration0 - Day 2: Vision–Language Models• CLIP concepts
• Cross-attention
• Text–image retrieval
• Training principles
• Hands-on: Use a vision-language model0 - Day 3: Audio & Speech Integration• Audio embeddings
• Speech recognition pipelines
• Audio–text systems
• Multimodal challenges
• Hands-on: Audio–text demo0 - Day 4: Multimodal Transformers• Unified transformers
• Cross-modal attention
• Zero-shot learning
• Transfer learning
• Hands-on: Build a multimodal classifier0 - Day 5: Applications & Innovations• Assistive technologies
• Content generation
• Robotics
• AR/VR multimodal systems
• Capstone project0
Instructor
Lessons:
