Course Outline

Introduction to Multimodal Learning

  • Overview of multimodal AI
  • Challenges in multimodal data processing
  • Benefits of 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 in 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 better 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

  • An understanding of machine learning and neural networks
  • Experience with Python programming
  • Familiarity with data preprocessing for various data types (text, image, audio)

Audience

  • Data scientists
  • Machine learning engineers
  • Software developers
  • Researchers focusing on AI and natural language processing
 14 Hours

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