Special Session : « Artificial Intelligence & IoT:  from Theory to Practice ».

In this intensive workshop, participants will delve deep into the world of large language models (LLMs) with a specific focus on supervised fine-tuning and retrieval augmented generation (RAG). As AI-driven natural language processing continues to reshape countless industries, understanding and mastering these techniques is paramount for researchers, data scientists, and developers alike.

Key Objectives:

• Foundation Concepts: Begin with an introduction to the core principles underlying LLMs, supervised learning, and retrieval-based models.
• Comparative Analysis: Explore the differences between traditional fine-tuning and retrieval augmented generation, allowing participants to discern the most suitable approach for various applications.
• Pros and Cons: Understand the strengths and limitations of each method, ensuring informed decisions in research and application.
• Hands-on Implementation: Engage in practical coding sessions where participants will implement supervised fine-tuning and RAG using Python, leveraging popular libraries and datasets.

Workshop Highlights:

• Real-world examples illustrate the impact and relevance of both supervised fine-tuning and RAG in modern AI solutions.
• Coding sessions to guide participants through common pitfalls and best practices.
• Interactive Q&A sessions for personalized guidance and deeper understanding.

Who Should Attend?