Introduction to Deep Learning

  • Gießen

Educators: 
Dominik March, Lukas Beierle, Sonja Diedrich, Julian Hahnfeld (BiGi)

Date:  
December 8th & 9th, 2025

Location: 
Gießen Seltersweg 85, Bioinformatics Lab

Contents:
In the first part of the course, we will cover the fundamental concepts and applications of deep learning, including model architectures, training procedures, and data preprocessing. In the second part, the focus will shift to applying deep learning models to bioinformatics tasks, using both custom-built and pre-trained models.

Learning goals:

  • Getting an overview of the topic ‘Deep Learning’ (with bioinformatic examples)
  • How to implement and train a neural network using Keras
  • Data preprocessing and encoding for neural network models
  • Large Language Models (LLMs) and pre-trained models and how they can be applied to various tasks.

Prerequisites:

  • Basic bioinformatics knowledge
  • Good knowledge of Python and the Linux Terminal
  • We recommend, that you bring your own computer, which should run Linux or MacOS
  • If you bring your own computer, it should ideally have a CUDA compatible graphics card

Keywords:
Deep Learning, Keras, Large Language Models

Tools/ Libraries/ Languages:
Python, Keras, Kerashub

Contact & Registration:
Email: This email address is being protected from spambots. You need JavaScript enabled to view it.

Organizational:

  • The number of participants is limited to: 20
  • Registration via email (see above)
  • We cannot cover any travel cost and/or accommodation costs
  • We provide catering during the course.
  • We will use Python as programming language with the library keras

 

Appendix:

- Micromamba (optional):

- Keras:

- CUDA setup (optional):

- Learning material: