When gradient descent is a kernel method
This really brings me back to my days in college, when this was the exact sort of stuff that ML classes focused on. You could have an entire course on various interpretations and extensions of kernel based methods. I wonder how much these insights are worth anymore in the age of LLMs, deep neural networks. I havent kept up too much with the NTK literature but it seems like the theoretical understanding of kernel based methods, gaussian processes does not confer you any advantage in being a better modern ML (specifically working on LLMs) engineer, where the skill set is more heavily geared towards systems engineering and/or devops for babysitting all your experiments.