OT-AIAT 21

Introduction to Deep Learning

Zielgruppe

This course is designed for software developers, data scientists, and researchers who want to transition from simply using AI tools to building, customizing, and deploying them.

This course is open and free of charge for all participants from academia, industry, and public administration fromEUand/orEuroHPC JUmember countries.

Inhalt und Ziele

This course covers the essentials of neural networks, from ANNs to CNNs for computer vision, RNNs for sequence processing and transfer learning. Participants will gain hands-on experience using PyTorch to build and train models on high-end GPUs on an HPC cluster.

Deep Learning (DL) has enjoyed a surge in popularity during the last decade. This is mainly owed to the fact that DL is computationally quite expensive and needs vast amounts of data to train useful models. Both computational resources and large datasets have become more readily available in recent years, leading to breakthroughs in DL applications.

This course aims at interested participants from all domains, who have not yet jumped on the bandwagon of the DL trend.

The course will cover the basic principles of fully connected neural networks, convolutional neural networks (CNNs) for computer vision, recurrent neural networks (RNNs) for text or speech recognition, transfer learning to leverage pretrained models, and touch on large language models (LLMs) which have become all the rage in the past few years.

Agenda & Content

For a detailed timetable and additional information, please seeAgenda & Contentin the left menu.

Trainer

Zeit und Ort

Seminarnummer:

OT-AIAT 21

Trainingsform:

Online-Tr. / Webinar

Dauer:

1 Tag(e) (7 Stunden)

Termin:

23.11.2026

Stundenplan:

09:00 - 16:00

Ort:

Online

Anmerkungen

Entry level & prerequisites

Basic – no prior DL knowledge is required.

Participants are expected to have basic programming skills in Python.
Participants should know how to work on the Linux command line.

Course format

This course will be delivered as a LIVE ONLINE COURSE (using Zoom).

Language

English

Hands-on labs

All participants will get a temporary user account on one of the ASC systems to do the hands-on labs.

You will use your own laptop or workstation to connect conveniently from your browser to the ASC Jupyterhub and do the hands-on exercises on a suitable CPU or GPU partition of the ASC.

Accepted participants will be contacted a few days before the course and asked to do a short pre-assignment that has to be completed before the course starts.

Lecturer

Simeon Harrison (AI Factory Austria AI:AT)
Simeon worked as a high-school mathematics teacher for eight years before joining the EuroCC project’s training team at ASC, TU Wien in February 2021. In that role, he designed, developed, and delivered courses on Machine Learning and Deep Learning. He currently works as an AI Expert for INiTS, Vienna’s high-tech start-up incubator, which is part of the AI Factory Austria consortium. There, he consults with and develops proof-of-concepts for start-ups and SMEs, with a special emphasis on training and fine-tuning Large Language Models (LLMs) on HPC clusters.

Organisation

Seminarleitung:

Buchwinkler David BA MA

Sekretariat:

Fröhlich Elisabeth