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???
For the past few weeks, I’ve been working with the LA Mayor’s Office to build some models to create new data columns on their Open Data Portal. In this post, I’m hoping to shine some light on LA’s Open Data Portal, as well as demystify the process of ML a little bit by walking ya’ll through my typical workflow.
Continue reading “LA Public Data: An Intuitive ML Walk-through”
Hackathon #2. UCLA. 1000 people. I wanted to go big or go home. My team Nihar Sheth, Markie Wagner, and Zane Durante, felt the same.
After a series of disputes and disagreement, we eventually found the perfect idea: using ML to improve frame rate of videos, by predicting what frame should come between any two given frames. It was complex, it was visual, it was potentially boom, and it was definitely potentially bust: Continue reading “Aper.io: 36 hours of Pizza Rat”
There are a number of research teams working on the problem of skin cancer detection, many of which have near human accuracy within laboratory data sets. There are also a large number of applications released that are used to quantify risk of skin cancer available today. So why am I trying to reinvent the wheel?
It’s kind of this mystical thing, a “hackathon,” to new programmers. It’s something that everybody mentions casually, you technically understand what it is, but it just feels so alien, at least to me. Maybe that’s just my Tennessee showing. Continue reading “CleanBeats.ai: My First Hackathon :)”