LLMs in Multimodal Applications

• 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

The integration of different types of data such as text, image, and audio represents the forefront of LLM applications, enabling more comprehensive and context-aware AI systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level data scientists, machine learning engineers, and software developers who wish to apply Large Language Models (LLMs) to multimodal data for advanced AI applications.

By the end of this training, participants will be able to:

• Understand the principles of multimodal learning with LLMs.
• Implement LLMs to process and analyze text, image, and audio data.
• Develop applications that leverage the strengths of multimodal data integration.
• Evaluate the performance of multimodal LLM systems.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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