| Type | Lab (basic level) |
| Term | WS 2024 |
| Mentor(s) |
Stefan Decker |
| Assistant(s) |
Sulayman K. Sowe Rezaul Karim, Ph.D. Yongli Mou |
Winter Semester Registration Closed!
Large Language Models (LLMs), such as GPT, Claude and Llama, are powerful tools that have transformed the landscape of Natural Language Processing (NLP), enabling advanced applications in various fields. The “Building Large Language Model Applications,” course is a practical, hands-on course designed to provide students with in-depth knowledge and experience in developing applications utilizing LLMs. Throughout the course, students will work on real-world projects and learn how to design, implement, and deploy advanced LLMs systems.
Learning Objectives:
By the end of this course, students will be able to:
- Understand the architecture and the core principles behind LLMs and their applications.
- Effectively preprocess and prepare data for training LLMs.
- Develop and fine-tune LLMs for specific use cases.
- Build and deploy robust NLP applications utilizing LLMs.
- Address ethical considerations and implement best practices in the development of LLM applications.
Assessment: Assessment will be based on project work, participation in practical sessions, and a final presentation demonstrating the developed application.
Recommended prior knowledge: Theoretical Knowledge in Deep Learning, Natural Language Processing; Practical Knowledge in Python (PyTorch, Transformers, LangChain), Databases (Relational database, Vector database and Graph database), and Web development
- Basic knowledge of machine learning and natural language processing.
- Proficiency in programming, particularly in Python.
- Familiarity with machine learning frameworks such as TensorFlow or PyTorch is recommended.
How SPP works:
