mmmmpancakes

8 ms·

1.
by mmmmpancakes 2y ago
So this is just showing a bit of your ignorance of stats. The general notion of compound risk is not specific to MSE loss. You can formulate it for any loss function, including L1 loss which you seem to prefer. Steins paradox and James Stei
2.
by mmmmpancakes 2y ago
No it is not bogus, you just don't know much stats apparently.
3.
GroceryVision Dataset(grocery-vision.github.io)
1 points by mmmmpancakes 2y ago|0 comments
4.
by mmmmpancakes 3y ago
parquet is perfectly fine > There is Parquet. It is very efficient with it’s columnar storage and compression. But it is binary, so can’t be viewed or edited with standard tools, which is a pain. I can open parquet in excel
5.
by mmmmpancakes 3y ago
To me, Polars feels like almost exactly how I would want to redesign Pandas interfaces for small - medium sized data processing, given my previous experience with Pandas and PySpark. Throw out all the custom multi index nonsense, throw out
6.
by mmmmpancakes 3y ago
Suggest the following pattern: 1. load and process / aggregate in polars to get the smaller dataset that goes into your plot. 2. df.to_pandas() 3. apply your favourite vis library that works with pandas. There's no use case i can
7.
by mmmmpancakes 3y ago
i mean, if you are reading and writing csv then yeah, you've already fucked up.
8.
by mmmmpancakes 3y ago
Scientific fraud makes me sad and angry.
9.
by mmmmpancakes 3y ago
Disagree. The purpose of a textbook and a lecture is very different. A good textbook can be a helpful resource for teaching and lecturing, but it is not sufficient to guarantee high quality math education. Conversely, a good educator who d
10.
by mmmmpancakes 3y ago
The ability to communicate math this way is honestly rare. It comes from a combination of deep understanding, long experience in communicating math, and a certain level of "culturing" that is specific to the academic experience. A
11.
by mmmmpancakes 3y ago
Because with real world data like in production in tech there are so many factors to account for. Brittle methods are more susceptible to unexpected changes in the data or unexpected ways in which complex assumptions abut the data fail.
12.
by mmmmpancakes 3y ago
> This seems like a natural feature of any sensitive method, not sure why this is something to complain about. I am exactly complaining it is sensitive. If theres robust alternatives why would i put this in prod?
13.
by mmmmpancakes 3y ago
Yeah, the only common theme I see in causal inference research is that every method and analysis eventually succumbs to a more thorough analysis that uncovers serious issues in the assumptions. Take for instance the running example of catho
14.
by mmmmpancakes 3y ago
From my experience propensity scores + ipw really doesn't get you far in practice. Propensity scoring models rarely balance all the covariates well (more often, one or two are marginally better and some may be worse than before). On to
15.
by mmmmpancakes 3y ago
It may be ideal to have all these items checked off. I think a productive way to look at this is "how many of these items does the roadmap check"? If the answer is "very few" then that might be an early warning sign you&
16.
by mmmmpancakes 3y ago
thanks
17.
by mmmmpancakes 3y ago
can you please spell out what MDL is an acronym for?
18.
by mmmmpancakes 3y ago
I mean, of course? Concurrency != parallelism, so that makes sense. Where multiprocessing shines is when you have an algorithm that can be fully parallelized and represented in a baby map-reduce framework, where the data being sent to each
19.
by mmmmpancakes 3y ago
I mean, hopefully why you might need multiprocessing in python is clear? If you have a python task that is highly parallelizable on a single machine with multiple cores, then multiprocessing is probably the right tool to quickly see if you
20.
by mmmmpancakes 3y ago
how do you work 4 years on a project before having a tracked goal. absurd. sounds like management's fault.
21.
by mmmmpancakes 3y ago
fancy name for what seems like a fairly standard "intro to proofs" textbook, at least based on the ToC.
22.
by mmmmpancakes 3y ago
> The fact that Microsoft is baking GPT into all of their products guarantees explosive growth. Why?
23.
by mmmmpancakes 4y ago
> I still think Elon is a smart guy. > I don't think he realizes that the techniques that work for cars and rockets don't work in social media. Hmmm...
24.
by mmmmpancakes 4y ago
Amazon has roughly 350 MM products if you account for third party sellers, so yeah, the scale is massively different.
25.
by mmmmpancakes 4y ago
Not only this but Amazon is a marketplace for buyers AND sellers whereas it appears McMaster-Carr is the seller. Sellers on Amazon are also Amazon customers who want their own pages, copy, etc.
26.
by mmmmpancakes 4y ago
The problem with your comparison to Amazon is that the scale of products offered on Amazon is completely different. A catalogue system like this is only viable if the number of products is relatively small, as appears to be the case of mcma
27.
by mmmmpancakes 4y ago
Confidently incorrect.
28.
by mmmmpancakes 4y ago
It goes without saying that quality of research is very important. However, getting a TT position is much more about connections and fashion than people think, especially in a field like pure math where it's much more difficult to argu
29.
by mmmmpancakes 4y ago
I think that's a very strange thing to say. I am not sure which priorities you are referring to, but I think I have a good guess, and I think they're ridiculous things to expect that aren't / shouldn't be correlated
30.
by mmmmpancakes 4y ago
Same here but for math. I did a survey of who was getting the TT jobs I wanted in the cities I wanted to live in and the trend was that they all went to Harvard / Princeton with a few exceptions. Seeing how strongly those profile eleme

More ›