Implementing Graph Convolutional Networks and Information Retrieval Mechanisms using pure Python and NumPy

Nov 21, 2021 1 min read

About this Project

When I was teaching myself machine learning, I wanted to make sure that I fully understood the logic and backpropagation calculus behind all of the network architectures and training techniques that I implemented.
This project uses pure Python and NumPy to implement Graph Convolutional Networks with global and hierarchical architectures as well as various graph pooling methods.
The models were trained to classify compounds using the Adadelta algorithm for gradient descent.

GitHub

John was the first writer to have joined pythonawesome.com. He has since then inculcated very effective writing and reviewing culture at pythonawesome which rivals have found impossible to imitate.