Recent submissions

Comment · 8 months ago
my brother in christ please take pity on my lack of backlinks. that said i do genuinely believe this, which is why i wrote it, and also want to hear the opinion of others (so i can make something useful).
Comment · 8 months ago
Comment · 2 years ago
Maybe you could start by replying to my email? I would not have complained publicly if there had been any form of communication from you but there was none.
Comment · 2 years ago
infoproc dot co. which is currently down.
Comment · 2 years ago
Comment · 2 years ago
the name is fly.io. it's in the title
Comment · 2 years ago
Thanks for this tip. I tried it and I keep getting the error: "Error: Your account got flagged by our internal fraud protection service" even after verifying my credit card.
Comment · 2 years ago
a personal vpn and a company website
Comment · 2 years ago
Comment · 2 years ago
This looks very interesting! I'm trying to understand if the flattening technique might work for my ts. It's structured as follows: At each time step t, I have an m by n data matrix. The value for m (rows) varies per time step. n stays constant and represents the features. And i want to predict one of the n values. (In this case, t represents a single day, m (rows) represent the people that entered a store on that day, and n (cols) represent various features of the people. I want to predict one of those features, given the others.) The fact that it's a time series matters, because i expect the relationship to change over time. For instance some feature n[x] (person wears a yellow shirt) might be correlated with my target feature n[y] (person steals) but only in the summer. would it be possible to flatten this too? What would that look like?
Comment · 2 years ago
Feedback welcome :)

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