Don't go to the river: Build your own stream.
So in my recent readings of various ML media, from blog posts to published papers, I’ve just started to notice a trend. I’m growing more certain that the ML community is ignoring uncertainty, which is certainly not a good practice and it renders much their results quite uncertain.
In this post, I wanted to just go over a quick and easy method to use inverse probability to estimate the uncertainty in your model’s test-accuracy. Continue reading “Know Thyself: And Know Thine Uncertainty”
Hey ya’ll. Hope your summer is going well! In the season of peaches and watermelon, it’s easy to take things for granted. No one has experienced this more than the optimization algorithms used in, well, just about every single machine learning and computational problem.
I thought, for this post, I would dive into one of the classics: the Broyden Fletcher Goldfarb Shanno algorithm, also known as BFGS, named after these ballers right here.
A classic Japanese Katana, with a thickness of around 2-3 inches, has over 2000, hand-folded layers of steel. To put this into context, if you fold a sheet of paper 15 times, it will reach a height of 3 meters, or, in other words, Shaq with about 3 burritos on his heads. The swords were so powerful that foreigners would often find their blades shattered within seconds of a fight. So I guess the question on your mind is, what the hell does any of this have to do with Bayesian Statistics???
