And why use readable variable names? "aA=q_(c8z,fW8)"
Seriously though, almost all the examples in TFA are of rewriting existing code. It may be that Python is still best for the rapid dev iteration. Then sure, cross-compile into Rust via the LLM.
Plus, If we care about token usage counts, Python has a lot more opportunities for compact "import thing_I_need" than having to generate entire libraries in Rust.
I honestly expected the demand to be "Release GTA 6 soon or else we will". ...The fact that they're just demanding money is a little disappointing. ;-)
Read to the end and... what was the point of that?
Where's the payoff?
There was a claim near the top that some things are easier to compute when viewed as cracovians. then some explanation, then suddently it switches to numpy and showing the time is the same.
New title: "Cracovians are a Waste of (the Reader's) Time"?
Paraphrase: ML in Julia is for Serious Scientists Doing Very Sciencey Things. Things you wouldn’t understand. But your desire to train deep learning models on GPUs and deploy them as apps elsewhere? ...Aw, c’mon, that’s boring. You can just use PyTorch for that. Now, who's up for a new round of Neural ODE benchmarks?!
Agreed. Is this an example of the patriarchal bias of science leaving women out of the telling of history -- finally being set straight by our friends at NPR -- or leftist revisionism trying to ascribe extremely inordinate influence to one member of a large team just because she happened to be female?
If one is assigning parentage, would Lyman Spitzer be the "father" of the Hubble telescope? Perhaps it had many "fathers" but only one "mother"?
EDIT: MichaelMoser123's added info and quotes are helpful. Merits updating the Wikipedia page to give her greater(/any) credit.
> Nobody cares how long it takes to train a model.
LOTS of people care how long it takes to train a model. A few minutes, vs. a day, vs. a week, vs. a month? Yea, that matters.
Think about how long it takes to try out different hyperparameters or make other adjustments while conducting research...
If you're Google maybe you don't care as much because you can fire off a hundred different jobs at once, but if you're a resource-limited mere mortal, yea, that wait time adds up.
I was really hoping to see a summary comparison of the performance(s) of the different models at the end, e.g. accuracy vs. complexity vs. execution time, etc.
Here's a summary from the end of each section...
1. TextCNN: "This kernel scored around 0.661 on the public leaderboard."
"By defining \mathbb{E}\left[\mathbf{x}\right]=\muE[x]=μ, ...and using the linearity of the expectation operator \mathbb{E}E, we easily arive [sic] to the following conclusion..."
Yikes. You don't define that \mathbb{E} was an 'expectation operator', or what an expectation operator even does, or the fact that it's linear. The v's disappeared somehow from inside the square brackets -- maybe you meant \muE[v]=μ?
So far this "tutorial" isn't defining its terms very well. I'm lost and it's only the very beginning.
There once was a documentary about a small group of British intellectuals (who also happened to be musical virtuosos) in which one of them asserted that the decision boundary between clever and stupid can have arbitrarily small width.
Reading this is surreal, like Opposite World. You are selling me on quiet, wifi & power, at the price of your fancy coffee (and preferably even real food), so I can work/read/study in nice atmosphere. Why else would I tolerate paying nearly $6 for a latte and $5 for a muffin? If all I want is to get coffee itself and immediately hit the road, I can go to coffee stand or vending machine.
"Your $3 coffee doesn't entitle you to sit for an hour." First, perhaps I'm typically more than the average user, $3 coffee sounds either horrid or a miracle. Second: Why not? Isn't that the whole point? You're luring me to come in & buy your wares so that I get the benefit of sitting for a while and do my thing, whether it's read a book or work on my laptop.
To the people who see silence & laptops as bad in and of themselves: who are you to prioritize talkers over non-talkers? In my mind and those of many near-university dwellers, cafes are precisely for sitting, reading, working, not for having (sometimes loud) conversations that disturb those around you.
Finally, I'm not aware of "taking up space", as there are typically multiple empty tables at the (independent, non-Starbucks) coffee shops I go to. Maybe this article applies to high-volume areas & times, but I'm not interested in such places anyway.
TL/DR: If you ban people working and studying on laptops, that will just be even less revenue for your establishment, because that is the only reason many of us showed up. Ration out the wifi with time limits and access codes on receipts if you like, I'm fine with that, otherwise just be happy I'm there rather than having yet another empty table.
Re. blocking scrapers: Some of us are neither vast corporate espionage practicioners nor zombie-botnet users: we're on our own, scraping for data science & other academic research purposes.
Is there some way to declare, "I am a legitimate academic user", something akin to 'TSA Pre' status?
"Sure, register for & use the site's API," you'll say. What if they don't have one?
"Sure, just don't slam the server with too many requests in a short time," you'll say. But if they're rejecting you just because they detect you're headless, etc...?
Seriously though, almost all the examples in TFA are of rewriting existing code. It may be that Python is still best for the rapid dev iteration. Then sure, cross-compile into Rust via the LLM.
Plus, If we care about token usage counts, Python has a lot more opportunities for compact "import thing_I_need" than having to generate entire libraries in Rust.