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