Schema della sezione

  • While the first Part was devoted to the mechanics of Deep Learning, this second part will be focused on two architecture families that incorporate useful inductive bias in specific domains:

    •  CNNs for images 

    •  RNNs for sequences

    We will review the basics of these architectural domains and we will develop practical skills to code - in PyTorch - a few selected models.

    Only with this practice the students will be able to perform independent work in deep learning problems.

    Towards the end of this Part we will start looking at several aspects of “attention” in deep networks, that will culminate with an implementation of a simple sequence to sequence model with explicit attention.

    If we have time we will also briefly describe models endowed with attention and memory: Neural Turing Machines and variations.

    The Keynote lecture of this part will address density estimation of representations: this is a powerful unsupervised technique to analyze general datasets, and in particular representations in deep networks.