That's just my perception, therefore "imo". I tried GOS, I've read the forum (about microG, F-Droid) and that's what I've felt: something shady. It is most likely just me, so I stay away from the project. I just wanted…
I don't buy into the "good guy" narrative associated with GrapheneOS. The complaint is valid, but the solution isn't to keep using AOSP. GrapheneOS project, for all its merits, limits users' freedom by forcing them to…
brilliant!
it is: (async function(oracle, punch, delay) { async function sleep(ms) { return new Promise((resolve, _) => { setTimeout(resolve, ms) }) } for (let i = 0; i < 100; i++) { const state = oracle._state const block =…
pure random player: (async function(punch, delay) { async function sleep(ms) { return new Promise((resolve, _) => { setTimeout(resolve, ms) }) } for (let i = 0; i < 100; i++) { punch(['L', 'R'][(Math.random() * 2) | 0])…
Who does that algorithm really serve?
great work! just a note regarding tf-idf, when you mention log10: i think you're missing the point on the reason of log and most importantly base 10. namely, using log10 gives us a perspective on the number of digits of…
> for index, item in enumerate(menu): should be > for index, item in enumerate(menu, start=1): for the example to be correct :)
yes, i see this as well. at first it was confusing, but later i understood it was the output.
My understanding is that the neural network is used to predict the "magnitude" of each eigenvector/axe (extracted by using PCA) in order to reconstruct an approximation of the original behavior.
That's just my perception, therefore "imo". I tried GOS, I've read the forum (about microG, F-Droid) and that's what I've felt: something shady. It is most likely just me, so I stay away from the project. I just wanted…
I don't buy into the "good guy" narrative associated with GrapheneOS. The complaint is valid, but the solution isn't to keep using AOSP. GrapheneOS project, for all its merits, limits users' freedom by forcing them to…
brilliant!
it is: (async function(oracle, punch, delay) { async function sleep(ms) { return new Promise((resolve, _) => { setTimeout(resolve, ms) }) } for (let i = 0; i < 100; i++) { const state = oracle._state const block =…
pure random player: (async function(punch, delay) { async function sleep(ms) { return new Promise((resolve, _) => { setTimeout(resolve, ms) }) } for (let i = 0; i < 100; i++) { punch(['L', 'R'][(Math.random() * 2) | 0])…
Who does that algorithm really serve?
great work! just a note regarding tf-idf, when you mention log10: i think you're missing the point on the reason of log and most importantly base 10. namely, using log10 gives us a perspective on the number of digits of…
> for index, item in enumerate(menu): should be > for index, item in enumerate(menu, start=1): for the example to be correct :)
yes, i see this as well. at first it was confusing, but later i understood it was the output.
My understanding is that the neural network is used to predict the "magnitude" of each eigenvector/axe (extracted by using PCA) in order to reconstruct an approximation of the original behavior.