Rapid Application Development with Large Language Models (LLMs) [HeFDI Code School: EXPERT TRACK]

Die Anmeldung für den HeFDI Code School Expert Track mit Schwerpunkt KI und LLMs läuft! Mit unserem Expert Track richten wir uns an fortgeschrittene Entwickler*innen, die ihre Fähigkeiten in einem spezifischen Feld der Softwareentwicklung verbessern möchten. Die beiden Ganztages-Workshops finden in Kooperation mit hessian.AI und unterstützt durch NVIDIA statt.

18.09.2025 (09:00h-17:00h) | Fundamentals of Deep Learning (online)\
HeFDI, hessian.AI | Speaker: Ben Lohmann (hessian.AI)\

25.09.2025 (09:00h-17:00h) | Rapid Application Development with Large Language Models (online)\
HeFDI, hessian.AI | Speaker: Kajol Raju (hessian.AI)\

Alle Kurse finden online und auf Englisch statt, die Teilnahme ist kostenlos, aber auf 100 Teilnehmende pro Kurs beschränkt.

Workshop #1: Fundamentals of Deep Learning

Speaker: Ben Lohmann

Description:
Businesses worldwide are using artificial intelligence to solve their greatest challenges. Healthcare professionals use AI to enable more accurate, faster diagnoses in patients. Retail businesses use it to offer personalized customer shopping experiences. Automakers use it to make personal vehicles, shared mobility, and delivery services safer and more efficient. Deep learning is a powerful AI approach that uses multi-layered artificial neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, and language translation. Using deep learning, computers can learn and recognize patterns from data that are considered too complex or subtle for expert-written software.

Learning Objectives:
- Learn the fundamental techniques and tools required to train a deep learning model
- Gain experience with common deep learning data types and model architectures
- Enhance datasets through data augmentation to improve model accuracy
- Leverage transfer learning between models to achieve efficient results with less data and computation
- Build confidence to take on your own project with a modern deep learning framework

Further details and Prerequisites: https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+C-FX-01+V3
 

Workshop #2: Rapid Application Development with Large Language Models (LLMs)

Speaker: 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: https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+C-FX-09+V2

Weitere Informationen: https://www.uni-marburg.de/en/hefdi/data-events/code-school \
Registrierung: https://uni-marburg.de/yhVlQP