LLMs in Multimodal Applications Training Course
The integration of diverse data types, including text, images, and audio, represents the cutting edge of Large Language Model (LLM) applications, paving the way for more comprehensive and context-aware AI systems.
This instructor-led, live training (available online or onsite) is designed for intermediate-level data scientists, machine learning engineers, and software developers who wish to apply Large Language Models (LLMs) to multimodal data to build advanced AI applications.
By the end of this training, participants will be able to:
- Grasp the core principles of multimodal learning with LLMs.
- Implement LLMs to process and analyse text, image, and audio data.
- Develop applications that harness the advantages of multimodal data integration.
- Evaluate the performance of multimodal LLM systems.
Course Format
- Interactive lectures and discussions.
- Ample exercises and practice sessions.
- Hands-on implementation in a live-lab environment.
Course Customisation Options
- To request a customised training for this course, please contact us to make arrangements.
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 Optimising Multimodal Systems
- Performance metrics for multimodal LLMs.
- Optimisation 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
- A solid 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.
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
LLMs in Multimodal Applications Training Course - Enquiry
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