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Course Outline
Introduction to Multimodal Learning
- Overview of multimodal AI.
- Challenges associated with multimodal data processing.
- Benefits of employing multimodal LLMs.
Understanding Large Language Models
- Architecture of state-of-the-art LLMs.
- Training LLMs with multimodal data.
- Case studies: Successful multimodal LLM applications.
Processing Multimodal Data
- Data preprocessing techniques for text, image, and audio.
- Feature extraction and representation learning.
- Integrating multimodal data within LLMs.
Developing Multimodal LLM Applications
- Designing user interfaces for multimodal interaction.
- LLMs in virtual assistants and chatbots.
- Creating immersive experiences with LLMs.
Evaluating and Optimizing Multimodal Systems
- Performance metrics for multimodal LLMs.
- Optimization strategies for enhanced accuracy and efficiency.
- Addressing bias and fairness in multimodal systems.
Hands-on Lab: Building a Multimodal LLM Project
- Setting up a multimodal dataset.
- Implementing a multimodal LLM for a specific use case.
- Testing and refining the system.
Summary and Next Steps
Requirements
- A foundational understanding of machine learning and neural networks.
- Proficiency in Python programming.
- Familiarity with data preprocessing techniques for various data formats (text, image, and audio).
Audience
- Data scientists.
- Machine learning engineers.
- Software developers.
- Researchers specialising in artificial intelligence and natural language processing.
14 Hours
