Educators:
Dominik March, Lukas Beierle, Sonja Diedrich, Julian Hahnfeld (BiGi)
Date:
Nov. 30 – Dec. 2, 2026
Location:
Gießen Seltersweg 85, Bioinformatics Lab
Contents:
In the first part of the course, we will focus on exploratory data analysis and data preprocessing. The second part will cover the fundamentals of deep learning, including model architectures and training procedures, and will shift to applying deep learning models to bioinformatics tasks. In the third part, you will evaluate and visualize the results, and you will learn about LMMs in biology and AI/GPTs in general.
Learning goals:
- Overview of the topic of deep learning (with bioinformatics examples)
- Data analysis and preprocessing for neural network models
- How to implement and train a neural network using Keras
- Large Language Models (LLMs) and pre-trained models and how they can be applied to various tasks.
Prerequisites:
- Good knowledge of Python and the Linux Terminal
- You can bring your own laptop, but it's not required
Keywords:
Deep Learning, Keras, Large Language Models
Tools / Libraries / Languages:
Python, Keras, Kerashub
Contact & Registration:
Email:
Organizational:
- The number of participants is limited to: 20
- We can not cover any travel costs and/or accommodation costs
- We provide catering during the course
- We will use Python as the programming language with the library Keras
Appendix:
- Keras:
* https://keras.io/getting_started/
* https://keras.io/getting_started/intro_to_keras_for_engineers/