Heavily involved in creating all weekly teaching materials, usually in the form of Jupyter notebooks, in which
the students were required to build their own deep neural networks from scratch in numpy in Term 1. Further, in Term 2,
I am the main contributor of tutorials on Pytorch/Tensorflow as well as cloud computing guides and GPU cluster debugging tutorials.
One of main teaching staff involved in designing/structuring the coursework. Furthermore, the main contributor
that implements any and all coursework boilerplate code/tutorial notes/guides.
Supervision of student groups on their deep learning projects in term 2, where students can choose any deep learning
related topic and work on a research project, to be implemented in Pytorch/Tensorflow. The main output is a report in
ICML paper format. This component requires supporting students in writing a concise, readable and compelling paper.