This tutorial provides a general introduction to the PyTorch Deep Learning framework with specific focus on Deep Learning applications for Precision Medicine and Computational Biology.
The main components of the framework will be presented, along with examples on focused topics (e.g. embeddings, few-shots learning, DL for BioMed and BioImages)
Outline of the Tutorial at a glance:
Exploring Gene Expression Data (using Tensors)
- Introduction to AutoGrad and Deep Neural Networks
Unraveling the Black boxes: Hidden Layers and Embeddings
- Short on latest about Interpretability
Few-shots learning and the curse of dimensionality
- Domain adaptation & friends
The rise of AutoEncoders for Medical Image Segmentation
- UNet and alike architectures
- Retina U-Net for Blood Vessel Segmentation
Affiliation: Fondazione Bruno Kessler / University of Bristol
Valerio Maggio is a Data Scientist and Post-doc Researcher. He has a Ph.D. in Computer Science from the University of Naples “Federico II”, and he is currently enrolled as Research/Cloud Software Engineer at FBK/MPBA. His research interests focus on Reproducible Science and Machine/Deep Learning methods for Computational Biology and Precision Medicine. Valerio is also a very active fellow in the Italian Python community and member of the organising committee of many Python Conferences (i.e. EuroPython, PyCon/PyData Italy, EuroSciPy).