Monday. 2-8 pm Berlin time


- Presenting Deep Learning (DL): the general picture and a little history

- Introducing a working DL model for image recognition

- Deconstructing the DL model for image recognition: the building blocks



Tuesday. 2-8 pm Berlin time


- Input data preparation: preprocessing and augmentation


- Cross-validation and performance measures


- Dealing with unbalanced data



Wednesday. 2-8 pm Berlin time


- Building a DL model for biological classification

- Example data and interactive solutions


Thursday. 2-8 pm Berlin time


 - Fine-tuning a deep learning model: impact of Learning Rate and Number of Epochs


 - Beyond classification: regression and segmentation



Friday. 2-8 pm Berlin time



- Recurrent Neural Networks


- Applications to -omics data




Discussing your own research problems with DL - POST COURSE SESSION