This post is part of the Collaborative Discussion 2 assignment for the Machine Learning module.
This post reflects on my initial post, peer responses, feedback, and summary post for the second collaborative discussion on AI writers, human oversight and responsible use.
Using Yu et al. (2024) as a comparison framework, I consider how conventional machine learning and deep learning could be evaluated for production-line visual inspection, and how their decisions could be explained to the people expected to act on them.
In this short exercise, I explored how gradient descent reduces a cost function by repeatedly updating model parameters.