[Online] Fundamentals of Deep Learning

Monday 21 Sept 2026, 09:00 → 17:00 Europe/Berlin

Online

Fundamentals of Deep Learning

Schedule & Format

• Date: 2026, September 21
• Time: 9:00 - 17:00 CE(S)T
• Format: Full-day
• Location: Online via Zoom
• Language: English

Registered participants will receive the video conferencing link via email on the day before the course.

Instructor

• Chang Liu, NHR@FAU, certified NVIDIA DLI Ambassador

This course is organized by Erlangen National High Performance Computing Center (NHR@FAU) in collaboration with NVIDIA Deep Learning Institute (DLI).

Course Description

Deep learning powers many of today's most impactful AI applications, from image recognition to large language models. This NVIDIA DLI course provides a practical introduction to the field through hands-on exercises in computer vision and natural language processing. Participants learn to build and train neural networks from scratch, apply data augmentation and regularization to improve accuracy, and make use of state-of-the-art pre-trained models via transfer learning.

Further information about this tutorial can be found on the NVIDIA DLI course page.

Prerequisites

Knowledge

• Python 3 programming experience, including functions, loops, dictionaries, and arrays
• Familiarity with Pandas data structures and basic statistics (e.g., computing a regression line)

Technical

• A free NVIDIA developer account

Course Structure

• Mechanics of deep learning: training a first model, convolutional neural networks, data augmentation
• Pre-trained models and large language models: image classification with transfer learning, LLMs for text tasks
• Final project: color image classification with small datasets, combining transfer learning and feature extraction

Learning Outcomes

After completing this course, you will be able to:

• Build and train neural networks from scratch for computer vision tasks
• Apply convolutional neural network (CNN) architectures to image classification problems
• Use data augmentation techniques to improve model generalization with limited data
• Leverage transfer learning with pre-trained models to achieve strong results efficiently
• Apply pre-trained large language models to text-based question answering tasks
• Design and execute a complete deep learning project from data preparation to model evaluation

Registration, Wait List and Withdrawal Policy

Registration

Please register at the bottom of this page. Registration is open until a few days before the course starts, or until the course is fully booked.

Prices and Eligibility

This course is open and free of charge for participants affiliated with academic institutions in European Union (EU) member states and Horizon 2020-associated countries.

Wait List

If the course reaches its maximum capacity, you can request to join the wait list by sending an email to nhr-training@fau.de. Please include your name and university affiliation in the message.

Withdrawal Policy

Please only register if you are committed to attending the course. No-shows will be blacklisted and excluded from future events.

If you need to withdraw your registration, please either cancel it directly through the registration system or send an email to nhr-training@fau.de.

Additional Courses

You can find an up-to-date list of all courses offered by NHR@FAU at https://hpc.fau.de/teaching/tutorials-and-courses/.