This tutorial provides a general introduction to Deep Learning using PyTorch with specific focus on challenges and solutions for Healthcare and Computational Biology.
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
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Unraveling the Black boxes: Hidden Layers and Embeddings
- Short on latest about Interpretability
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Few-shots learning and the curse of dimensionality
- Domain adaptation & friends
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The rise of AutoEncoders for Medical Image Segmentation
- UNet and alike architectures
- Retina U-Net for Blood Vessel Segmentation
some
Python Skill Level:expert
Domains:Artificial Intelligence, Deep Learning, Machine Learning, Science
Abstract as a tweet: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.