Deep learning in biology - online
General context
The goal of this two-day workshop is to get acquainted with the rapidly evolving deep learning techniques that exist for bio informatics and bio image informatics, for both predictive and explorative analysis.
Large amounts of data and compute resources have enabled the development of high-performance machine learning models. This is particularly due to deep learning techniques. By looking at many data samples, these models can find structure in the data that is useful for predictive and explorative analysis: e.g. classification, clustering, data generation, dimensionality reduction, etc. The most popular applications within biotechnology are concerned with image segmentation, diagnostics, sequence analysis, etc.
However, deep learning models are far from straightforward to implement correctly due to the many different hyperparameter settings, optimization procedures, architecture choices, etc.
In this course, we will make use of Jupyter Notebook and PyTorch, which are both based on Python, to apply deep learning techniques on both bio informatics and bio image informatics data. We aim to work towards applications that participants would like to study.
Basic knowledge of Python is required (and machine learning is recommended) to fluently participate in the theoretical and practical parts of the course. If you don't meet these requirements you should follow the Python introduction course. and/or the machine learning introduction course first.
Trainers
Joris Roels
