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!
Speakers: Valerio Maggio