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 Keras, 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.