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



Wednesday. 2-8 pm Berlin time


- Building a DL model for biological classification

 - Meet the assignment data

- Build your own DL model on the assignment data


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


- What didn’t fit in the course: current state of the art and cool applications


- Discussing your own research problems with DL