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Graph convolutional networks

Difficulty level

This tutuorial covers the concept of Graph convolutional networks and is a part of the Deep Learning Course at CDS, a course that covered the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. Prerequisites for this module include: Modules 1 - 5 of this course and Introduction to Data Science or a Graduate Level Machine Learning.

Topics covered in this lesson


00:00 – Welcome to class
00:20 – Recap from lecture 10 → Graph Transformer Networks (GTNs)
02:50 – Today plan: tensors/representations living on vertices and edges
03:20 – Self-learning resources with Xavier Bresson and Jure Leskovec
04:42 – Graph Convolutional Networks (GCNs)
23:38 – Connection with Convolutional Nets (CNNs) on grids
26:36 – Residual gated GCNs
37:26 – Domain sparsity note
38:27 – PyTorch implementation using Deep Graph Library (DGL)
56:44 – And that was it!