HeFDI Code School Expert Track: Rapid Application Development with Large Language Models (LLMs)
Second full day HeFDI coding workshop with hessian.AI in cooperation with NVIDIAs Deep Learning Institute (limited to 150 participants and academic students, staff, and researchers)
- https://www.uni-giessen.de/de/ueber-uns/veranstaltungen/fortbildung/hcs-expert-2-2025
- HeFDI Code School Expert Track: Rapid Application Development with Large Language Models (LLMs)
- 2025-09-25T09:00:00+02:00
- 2025-09-25T17:00:00+02:00
- Second full day HeFDI coding workshop with hessian.AI in cooperation with NVIDIAs Deep Learning Institute (limited to 150 participants and academic students, staff, and researchers)
25.09.2025 von 09:00 bis 17:00 (Europe/Berlin / UTC200)
Online, register here: https://uni-marburg.de/yhVlQP
Together with hessian.AI and supported by NVIDIA we offer two full day hands-on online expert workshops related to coding and AI. Both workshops are part of the NVIDIA Deep Learning Institute and are conducted by certified instructors. Participants of these workshops will learn the fundamentals of deep learning and get practical insights in rapid application development with Large Language Models (LLMs). These workshops are directed at researchers who already have experience with coding, but want to further develop their skills by diving into the vast field of code and AI. You find further details on the prerequisites for each workshop on the linked workshop page by NVIDIA below.
For active participation in the workshops and to receive a certificate of attendance signed by NVIDIA, you must register with NVIDIA's Deep Learning Institute. Further details will be provided during registration. Please note that participation is limited to academic students, staff, and researchers. When registering, ensure that your affiliation is clearly indicated through your email address.
We look forward to your participation!
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Workshop #2: Rapid Application Development with Large Language Models (LLMs)
Lecturer
Kajol Raju
Description
Recent advancements in both the techniques and accessibility of large language models (LLMs) have opened up unprecedented opportunities to help businesses streamline their operations, decrease expenses, and increase productivity at scale. Additionally, enterprises can use LLM-powered apps to provide innovative and improved services to clients or strengthen customer relationships. For example, enterprises could provide customer support via AI companions or use sentiment analysis apps to extract valuable customer insights. In this course you will gain a strong understanding and practical knowledge of LLM application development by exploring the open-sourced ecosystem including pretrained LLMs, enabling you to get started quickly in developing LLM-based applications.
The workshop covers large language models from beginning to end, starting with fundamentals of transformers, progression into foundational large language models, and finishing in model/agentic orchestration. Each of these sections is designed to equip participants with the knowledge and skills necessary to progress further in developing useful LLM-powered applications.
Learning Objectives
- Find, pull in, and experiment with the HuggingFace model repository and Transformers API.
- Use encoder models for tasks like semantic analysis, embedding, question-answering, and zero-shot classification.
- Work with conditioned decoder-style models to take in and generate interesting data formats, styles, and modalities.
- Kickstart and guide generative AI solutions for safe, effective, and scalable natural data tasks.
- Explore the use of LangChain and LangGraph for orchestrating data pipelines and environment-enabled agents.
Further details and prerequisites