Out of curiosity, how was it discovered? You would have to look for it to find this linear combination.
HN user
ninja3925
data scientist at a large tech company.
These are extremely high prices. In Texas, we pay 12-15c.
So i assumed it would get crushed by OPQ (which requires training)
The story is actually quite interesting. The Serbs observed that a nighthawk would routinely fly the same route but their radar couldn’t lock on it unless the missile hatch were open, which they managed to elicit.
In short, it took 2 rare events to occur for it to happen.
These signs are mostly on the highway
This seems to be incorrect. The correct way to combine measurements with various degree of precision is to use the inverse variance weighting law
Large cloud providers could offer that solution but then, crawlers can also change cycle IPs
Can you expand? What is “poison” referring to? Surely, burning coal as Germany’s current pace can’t be seen as a success, can it?
This is not how it works. The interviewer knows 1-2 problems and there is no time for profiling since they are rushing through their day, probably focused on their day to day work. You are the least of their concern, believe me.
Source: we am a hiring manager.
Very likely.
Just look at health insurance companies for a prime example: they make profit by denying claims
Despite being from Europe, I find this to be a shocking and erroneous interpretation.
Clearly, health insurances have the duty to allocate limited resources (“premiums”) across members. Denying and accepting claims is the mechanism to that end. Accepting all claims would increase premiums and reduce membership (by pricing people out). Would that an ideal state? Clearly not.
Note that it's not done for performance reason but rather to generate clear feature maps.
I are
Interestingly, FAISS does exactly that before doing Product Quantization and it works very well (errors are much lower compared to no rotation). They call it “optimal PQ”. During training time, they iterate to find a good candidate and save the best one.
Perhaps not entirely coincidentally, FAISS is also maintained by FB.
https://faiss.ai/cpp_api/struct/structfaiss_1_1OPQMatrix.htm...
The advised reader could devise a solution using public / private key where: - Alice communicates her public key - Bob communicates his - Alice encoders her cards with Bob’s public key - Bob does the same
Problem solved!
I actually think war driving progress is a strong argument. Not to use any obvious examples (e.g. internet), I would argue war has a profound effect on the psyche of nations (whether they lose or win).
A few examples: - Germany rose to power in the 1800s, with a culmination at Sedan in 1870, driven by the humiliation Napoleon inflected on 60 years earlier. In 1870, Germany was a behemoth of technology (especially chemistry) & industry - France won WW1 with such heavy losses that its people said “never again” (they called WW1 the “Great War” or the “last war”). War left an indelible mark (one wished it had left the same mark on the German).
Many such examples. Countries compete and war is a great impetus to modernize the Nation.
This is far fetched. It’s a very thoughtful article.
If you save 32x memory with binarization, why not do a projection to a larger dimension? Say 4096 for instance. Could this actually improve performance WHILE reducing memory?
a month.
No, It says a week in the article.
“Each aircraft takes about a week to have its AeroSHARK film applied, which requires high-precision workmanship from our personnel.”
It’s mind boggling how a country can be so rich as to spend millions of manhours on a project and never use it. Maybe we have become too rich as a species.
Column misnaming/typo is indeed a problem in pandas. I think a powerful IDE could do the trick though.
I wish the article made an attempt at providing a benchmark. I imagine that TSMC has a lot of prime age families so you can’t compare it against the overall population. Without it, one cannot make any inference in whether it’s low or high, rendering the article useless.
And the military:
https://www-users.cse.umn.edu/~arnold/disasters/patriot.html
The ability for the government to spend money it doesn’t have doesn’t cease to amaze. The deficit was $1.6T last year (over 5% of the GDP) but 2024 is starting strong.
This looks quite helpful actually. Thanks for sharing.
It can be done for performance reason I assume. Numpy for instance does that:
Thanks for posting this.
I work in data science and I haven’t seen anybody claiming that PCA, an unsupervised technique, is a good classification (supervised) technique. I think this is the framing that the author is pursuing.
That said, some candidates might mention clustering because it’s easy to understand and you can apply an action (this group is high but also good customers so they get a special treatment).
Why does the US have to get involved? Wouldn’t Arab countries, be more suited to conduct such operations?
True.
Also, you are probably doing it wrong by turning a matrix to matrix multiplication into a for loop (over rows). The optimal solution results in better performance
sim = np.vstack(df.col) @ vec
We use CLIP internally (large US tech company) and it works very well at a large scale