Exploring Weight Agnostic Neural Networks
Unpopular quote from my image and video processing professor - “The only problem with machine learning is that the machine does the learning and you don’t.”<p>While I understand that is missing a lot of nuance, it has stuck with me over the past few years as I feel like I am missing out on the cool machine learning work going on out there.<p>There is a ton of learning about calculus, probability, and statistics when doing machine learning, but I can’t shake the fact that at the end of the day, the output is basically a black box. As you start toying with AI you realize that the only way to learn from your architecture and results is by tuning parameters and trial and error.<p>Of course there are many applications that only AI can solve, which is all good and well, but I’m curious to hear from some heavy machine learning practitioners - what is exciting to you about your work?<p>This is a serious inquiry because I want to know if it’s worth exploring again. In the past university AI classes I took, I just got bored writing tiny programs that leveraged AI libraries to classify images, do some simple predictions etc.