LLMs in Multimodal Applications Training Course
Combining various data forms—such as text, images, and audio—marks the cutting edge of LLM applications, paving the way for more thorough and context-sensitive AI systems.
Designed for intermediate-level data scientists, machine learning engineers, and software developers eager to utilize Large Language Models (LLMs) with multimodal data for sophisticated AI solutions, this live, instructor-led training is available both online and in person.
Upon completing this training, participants will be able to:
- Grasp the core principles of multimodal learning using LLMs.
- Apply LLMs to process and analyze text, image, and audio data.
- Create applications that harness the full potential of multimodal data integration.
- Assess the performance of multimodal LLM systems.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practice sessions.
- Practical implementation within a live lab environment.
Customization Options
- For tailored training sessions, please reach out to us to arrange your needs.
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Duration 14 hours
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
- Knowledge of machine learning and neural networks
- Proficiency in Python programming
- Experience with data preprocessing for diverse data types (text, image, audio)
Target Audience
- Data scientists
- Machine learning engineers
- Software developers
- Researchers specializing in AI and natural language processing
Open Training Courses require 5+ participants.
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