Graph Networks in 2020

Graph Networks are extremely useful tools to help understand the graph data that's all around us. In this episode, I'm going to explain what some of the latest advancements in graph networks are and how you can leverage them to build your own graph network in a few lines of Python code. This space has matured so much that there is never a single library to discuss, there are always multiple competing options (which is a great thing). We'll be weighing the pros and cons of the Deep Graph, Graph Nets, and PyTorch Geometric library as well. I'm particularly interested in Graph Networks because of how well suited they are for interpreting the most complex biochemical factory that we know of - the human body. Enjoy! TWITTER: https://bit.ly/2OHYLbB WEBSITE: https://bit.ly/2OoVPQF INSTAGRAM: https://bit.ly/312pLUb FACEBOOK: https://bit.ly/2OqOhx1 Coding Challenge Submission link ( Due Jan 26, 2019 at 12 PM PST): https://forms.gle/Y3rY8EQhuyVySfx16 This is the example code from the video by the Deep Graph team, originally the toy dataset was the 'karate club' one, but i wanted to demo something more meaningful so i changed it to a toy 'protein network': https://docs.dgl.ai/tutorials/basics/1_first.html#step-1-creating-a-graph-in-dgl Type the code into Colab if you don't want to install it locally: https://colab.research.google.com/ Simple Neural Network by Milo Harper: https://gist.github.com/miloharper/62fe5dcc581131c96276 Subscribe for more educational videos! The Deep Graph Library: https://github.com/dmlc/dgl The Graph Nets Library: https://github.com/deepmind/graph_nets PyTorch Geometric Library: https://github.com/rusty1s/pytorch_geometric The Hyperfoods paper: https://www.nature.com/articles/s41598-019-45349-y Read any research paper for free by typing it into this: https://sci-hub.se/ Are you a total beginner to machine learning? Watch this: https://www.youtube.com/watch?v=Cr6VqTRO1v0 Learn Python: https://www.youtube.com/watch?v=T5pRlIbr6gg Learn