2022-08-30 –, HS 120
In this tutorial we will go through the main features of the PyTorch framework for Deep Learning.
We will start by learning how to build a neural network from the ground up, deep diving into torch.tensor, Dataset and optimisers.
We will analyse data cases from different domains (e.g. numerical, images), introducing different neural network layers and architecture. Last but not least, a few tips from a pure Data science-y perspective will be shared, to appreciate the wonderful integration PyTorch has with the Python Data model!
In this tutorial we will go through the main features of the PyTorch framework for Deep Learning.
We will start by learning how to build a neural network from the ground up, deep diving into torch.tensor, Dataset and optimisers.
We will analyse data cases from different domains (e.g. numerical, images), introducing different neural network layers and architecture. Last but not least, a few tips from a pure Data science-y perspective will be shared, to appreciate the wonderful integration PyTorch has with the Python Data model!
Introduction to PyTorch library for scientific computing
Domains –Machine Learning
Expected audience expertise: Domain –none
Expected audience expertise: Python –none
Valerio Maggio is a Data scientist, fellow at the Software Sustainability Institute, and a casual "Magic: The Gathering" wizard. He holds a Ph.D. in Computer Science with a thesis on Machine Learning for Software Maintainability, and was previously appointed Senior Research Associate at the University of Bristol. Valerio is well versed into open source software, and best software development practice, specifically focusing on scalable and reproducible machine learning pipelines. Valerio is an active member of the Python community: over the years he has led the organisation of many international conferences like PyCon/PyData Italy/EuroPython, and EuroSciPy.