LLMs in Multimodal Applications Training Course
Combining various data types like text, images, and audio is at the cutting edge of LLM applications, allowing for more thorough and context-sensitive AI systems.
This instructor-led, live training (available online or onsite) is designed for intermediate-level data scientists, machine learning engineers, and software developers aiming to utilize Large Language Models (LLMs) with multimodal data for sophisticated AI applications.
Upon completion of this training, participants will be able to:
- Grasp the principles of multimodal learning using LLMs.
- Apply LLMs to process and analyze text, image, and audio data.
- Create applications that capitalize on the advantages of multimodal data integration.
- Assess the performance of multimodal LLM systems.
Course Format
- Interactive lectures and discussions.
- Abundant exercises and practical work.
- Hands-on implementation in a live-lab setting.
Course Customization Options
- To request customized training for this course, please reach out to us to make arrangements.
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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
- Experience with Python programming
- Familiarity with data preprocessing for different 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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