About the event
This event is organised by: Mimer AI Factory & LUMI AI Factory (CSC)
Deep learning is a powerful subset of machine learning where computers learn patterns from data, similar to how our brains learn. It uses artificial neural networks – systems inspired by biological neurons that process information through many layers. The term “deep” refers to networks with tens or hundreds of layers, each containing millions of connections. Deep learning today powers technologies ranging from foundational applications such as language models and image recognition, to cutting edge applications such as weather forecasting and protein folding. However, for beginners, stepping into this field can feel daunting and we intend to make this easy for you.
This online workshop, organized by Mimer, in partnership with LUMI AI Factory, is designed to provide a beginner-friendly introduction to deep learning concepts, workflows, architectures, and practical applications. You will learn end-to-end approaches for:
-
tackling AI tasks including classification and regression
-
building deep model architectures such as Convolutional Neural Networks (CNNs)
-
apply advanced training techniques such as transfer learning
Who is this for?
- Students and early-career researchers in computer science, bioinformatics, natural sciences, engineering, or related fields
- Data scientists working on deep learning-based applications
- Aspiring software developers looking to learn foundational skills in AI
Key takeaways for participants:
A gentle introduction to deep learning fundamentals, covering:
- Core concepts and terminology
-
Steps in a deep learning workflow using Python and Keras (with Tensorflow, and potentially also with PyTorch as backend)
- Data preparation for training
- Implementing a basic neural network
- Monitoring and troubleshooting the training process
- Visualizing results and evaluating model performance
Prerequisites:
-
- Basic Python programming skills and familiarity with packages like NumPy, Pandas, and Matplotlib
- Experience working with Jupyter notebooks (recommended but not mandatory)
Schedule
All times in CET (Europe/Stockholm time)
| Day | Time | Contents |
|---|---|---|
| 2026-02-16 | 9:00 – 11:00 | Setup and dry-run |
| 2026-02-17 | 9:00 – 12:00 | Introduction; Classification by a neural network using Keras |
| 2026-02-18 | 9:00 – 12:00 | Monitoring the training process |
| 2026-02-19 | 9:00 – 12:00 | Advanced Layer types; Transfer learning |
Instructors and Teaching Assistants
- Ashwin Mohanan, Mimer AIF / RISE
- Francesco Fiusco, Mimer AIF / RISE
- Katja Mankinen, LUMI AIF / CSC
- Lodovico Giaretta, Mimer AIF / RISE
- Marlon Tobaben, LUMI AIF / CSC
- Oskar Taubert, LUMI AIF / CSC
- Yonglei Wang, Mimer AIF / NAISS