NYU Deep Learning Week 13 – Practicum: Graph Convolutional Networks (GCNs)

Join the channel membership: https://www.youtube.com/c/AIPursuit/join

Subscribe to the channel: https://www.youtube.com/c/AIPursuit?sub_confirmation=1

Support and Donation: Paypal ⇢ https://paypal.me/tayhengee Patreon ⇢ https://www.patreon.com/hengee BTC ⇢ bc1q2r7eymlf20576alvcmryn28tgrvxqw5r30cmpu ETH ⇢ 0x58c4bD4244686F3b4e636EfeBD159258A5513744 Doge ⇢ DSGNbzuS1s6x81ZSbSHHV5uGDxJXePeyKy

Wanted to own BTC, ETH, or even Dogecoin? Kickstart your crypto portfolio with the largest crypto market Binance with my affiliate link: https://accounts.binance.com/en/register?ref=27700065

Notebook for the practicum: https://github.com/Atcold/pytorch-Deep-Learning/blob/master/16-gated_GCN.ipynb Introduction to Graph Convolutional Network (GCN): https://atcold.github.io/pytorch-Deep-Learning/en/week13/13-3/ Source: https://youtu.be/2aKXWqkbpWg Subscribe to Alfredo Canziani: https://www.youtube.com/channel/UCupQLyNchb9-2Z5lmUOIijw

The video was published under the license of the Creative Commons Attribution license (reuse allowed). It is reposted for educational purposes and encourages involvement in the field of research.

In this section, we introduce Graph Convolutional Network (GCN) which is one type of architecture that utilizes the structure of data. Actually, the concept of GCNs is closely related to self-attention. After understanding the general notation, representation and equations of GCN, we delve into the theory and code of a specific type of GCN known as Residual Gated GCN. 0:00:47 – Introduction to Graph Convolutional Network (GCN) 0:16:32 – Residual Gated GCN Theory and Code 0:34:58 – Gated GCNs Implementation Code and Training ... https://www.youtube.com/watch?v=ULlHaJMI2W8