9. High performance machine learning
Content: In recent years machine learning and deep learning techniques, in particular, have developed tremendously. Neural networks are being used in more and more application domains going from computer vision to speech recognition, and even replacing parts of the compute pipeline for scientific HPC applications. In this course, you will learn how to use HPC infrastructures efficiently to get the best performance out of different machine learning tools with several hands-on sessions. We will touch upon the scalability challenges involved when using both large-scale data and large-scale models.
Note: Depending the background of the audience attending this workshop, we might split the workshop into two parallel sessions one for beginners and one for advanced users
- Duration: 8 hours
- Date and Time: see Schedule.
- Location: Science Park 904, Room: see Schedule.
- Target group: anybody who wants to learn how to run large machine learning tasks in the most optimal way
- Prerequisites: it's recommended to have some familiarity with machine learning and/or statistics, as well as the use of Python/Jupyter notebooks
- Course Leader: Maxwell Cai / Valeriu Codreanu / Sagar Dolas / Ruben Hekster / Caspar van Leeuwen / Damian Podareanu / David Ruhe (SURFsara).