LLMs in Multimodal Applications Training Course
The fusion of diverse data formats, including text, images, and audio, marks the cutting edge of LLM applications, paving the way for more holistic and context-sensitive AI systems.
This instructor-led, live training session (available online or on-site) targets intermediate-level data scientists, machine learning engineers, and software developers keen on applying Large Language Models (LLMs) to multimodal data to build advanced AI solutions.
Upon completion of this training, participants will be equipped to:
- Grasp the core principles of multimodal learning utilising LLMs.
- Deploy LLMs to process and analyse text, image, and audio data.
- Construct applications that capitalise on the advantages of integrating multimodal data.
- Assess the performance of multimodal LLM systems.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation within a live-lab environment.
Course Customisation Options
- To arrange bespoke training for this course, please get in touch with us.
Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
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
- Proficiency in Python programming
- Experience with data preprocessing for various data types (text, image, audio)
Audience
- Data scientists
- Machine learning engineers
- Software developers
- Researchers specialising in AI and natural language processing
14 Hours
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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