Some introductory ML notebooks illustrating different regression methods such as linear regression (LR), gaussian process regression (GPR), feed forward neural (FFN) networks, and graph convolutional neural networks (GCN) using a structure-property prediction problem.
- Basics: https://distill.pub/2021/gnn-intro/
- Overview of GCN: https://tkipf.github.io/graph-convolutional-networks/
- Implementation in PyTorch-Geometric: https://projects.volkamerlab.org/teachopencadd/talktorials/T035_graph_neural_networks.html