Introduction to Deep Learning for Biologists

Build and evaluate deep-learning models for biological prediction using Python and Jupyter notebooks. Lectures and collaborative exercises develop convolutional networks for classification, regression and image segmentation, with attention to preprocessing, augmentation, class imbalance, cross-validation and overfitting. Later sessions explore learning-rate and training-duration choices, recurrent networks and applications to omics data. Participants interpret model performance and discuss their own research problems.

2026-11-23–2026-11-27, 14:00–20:00 daily, Europe/Berlin (CET, UTC+01:00). Online course; fee €530. A biology background is expected; basic Python and Linux familiarity is helpful but not required.