Training
We qualify you in all aspects of AI. You can choose from an extensive selection of training courses that have been developed to match the services offered in KISSKI. The partners of the KISSKI consortium also open relevant areas of their own training programmes to KISSKI users.
Training courses explicitly designed for KISSKI
In order to register for the courses offered by the GWDG free of charge, you need an AcademicId, which you can easily create.
Introduction to AlphaFold
Date: 2024-10-29Venue: online
Organiser: GWDG
AlphaFold is a groundbreaking machine learning tool in the complex field of protein folding and provides a vital step in many bioinformatics and molecular simulation workflows.
Deep Learning Bootcamp: Building and Deploying AI Models
Date: 2024-11-04 - 2024-11-05Venue: Online
Organiser: GWDG
Introduction to neural networks, model building with TensorFlow and PyTorch and Deployment of AI models.
Secure HPC - Parallel Computing with Highest Security
Date: 2024-12-02Venue: Online
Organiser: GWDG
Understand the importance of Secure HPC for working with sensitive data and get familiar with the main steps.
Deep Learning Bootcamp: Building and Deploying AI Models
Date: 2024-12-09 - 2024-12-10Venue: Online
Organiser: GWDG
Introduction to neural networks, model building with TensorFlow and PyTorch and Deployment of AI models.
External courses offered by the KISSKI consortium partners
Thematically appropriate courses from the training courses offered by the institutions participating in the KISSKI consortium. Some of the courses are subject to a fee. In order to register for the GWDG’s course program free of charge, you need an AcademicId, which you can easily create.
Performance Analysis of AI and HPC Workloads
Date: 2024-10-22Venue: Online
Organiser: GWDG
This course will equip you with fundamental knowledge on how to efficiently use HPC systems to run AI applications.
AutoML - Automated Machine Learning
Date: Anytime (self studies)Venue: Online
Organiser: LUH
Learn how to use and design automated approaches for determining Machine Learning (ML) pipelines efficiently
An overview of past courses can be found here.