melondonkey

5 ms·

1.
by melondonkey 2y ago
Usually pretty scam savvy but dropped my guard and bought an absolute garbage AI translation of The Little Prince on Amazon. Now I research anything before buying
2.
by melondonkey 2y ago
I think it’s honestly annoying how they feel they have to parenthetically add every time something is a lie or untrue. While their intention is good I think it does a service to no one and underestimates the intelligence of their listeners
3.
by melondonkey 2y ago
Looks like Pokemon Jirachi
4.
by melondonkey 2y ago
The cultural divide between ML engineers and “girls and gays” in data science is very real and in my experience getting worse. Good but rare when the styles can be brought together.
5.
by melondonkey 2y ago
Damn, this is like the fifth time series framework posted this week. This one seems theoretically more interesting than some others but practically less useful. For one, who wants to do stuff in tensorflow anymore let alone tensorflow-prob
6.
by melondonkey 2y ago
Hard to meet everyone where they are and at the same time give them a relevant practical application for their own life. Good learners just soak it up and look for the application later. But that also doesn’t fit all. It’s hard to even w
7.
by melondonkey 2y ago
Data scientist here that’s also tired of the tools. We put so much effort in trying to educate DSes in our company to get away from notebooks and use IDEs like VS or RStudio and databricks has been a step backwards cause we didn’t get the
8.
by melondonkey 2y ago
One detail I don’t really understand is the low-variance normal component of the target mixture. Would be curious to see from the weights how often that was used
9.
by melondonkey 2y ago
I know. Here I am modeling my data generating process like a chump.
10.
by melondonkey 2y ago
Just needs a less engineering-oriented DS role and will be fine. Consulting is a good way to work in lots of industries and try things on.
11.
by melondonkey 2y ago
I guess I just mean I’m a data scientist—someone who uses models like these in practice as opposed to someone who develops them. I’m not sure what to even make of a term like “foundational time series”. Does that just mean it’s widely used
12.
by melondonkey 2y ago
As a practitioner the most impactful library for time series has been brms, which basically gives you syntactic sugar for creating statistical models in Stan. Checks all the boxes including probabilistic forecasts, multiple link functions
13.
by melondonkey 2y ago
Weird one minute it feels like the internet is screaming that I’m an out-of-touch dinosaur for using R and the next a simple link to its most popular IDE makes the front of HN.
14.
by melondonkey 3y ago
This is interesting. Are BART models differentiable? I haven’t looked closely at them but I would have thought for posterior sampling they’d have to be. BART has been around for a while, too
15.
by melondonkey 3y ago
What? Can you explain the mechanism than a NN can “extrapolate” an invoice where a tree model couldn’t? This is all just how the modeler builds the features. Also all models are a “mean of the subgroup of the data.” The prediction is by d
16.
by melondonkey 3y ago
More dashboards need this I think. I’ve also added relative standard error values on aggregations before to serve as a reliability filter that doesn’t even show users data when they slice it too then.
17.
by melondonkey 3y ago
At this point I wish every junior DS could read this paper and not come in to every problem with the new bright idea that they’re going to beat XGBoost with their DL architecture. Free promotion if they never say the words “latent subspace
18.
by melondonkey 3y ago
This explanation doesn’t make sense to me. What do you mean by “linearize your data”—tree methods assume no linear form and are not even monotonically constrained. Classification is not done by plane-drawing but by probability estimation