joker3

Except Mama Cass.

Finally I can take NumPy seriously now that it's been published.

Peter and his former student Dan McCarthy have done a lot of work on CLV calculations.

The output of a machine learning algorithm is code that no one was clever enough to write. In light of Kernighan's law, that should give you pause.

Is there ever a right time to relax meat safety rules?

NEXP is nondeterministic exponential time, right? In that case we know that P /= EXPTIME by the time hierarchy theorem and EXPTIME is contained in NEXP. We also have a direct result that NP /= NEXP (reference given in https://complexityzoo.uwaterloo.ca/Complexity_Zoo:N#nexp).

Is there a non-paywalled version?

Probably in 2030 we're going to see a similar but smaller hit to those sorts of schools. What effect that has is anyone's guess right now.

You think it's bad now? Wait until you see what happens in 2026.

As many of you remember, the bottom dropped out of the US economy in 2008. As a result, many people delayed having children for a few years (or just decided not to). That means that the freshman class of 2026 is going to be significantly smaller than that of 2025, and that's going to shut down a large number of small and financially strapped colleges.

It's difficult to imagine exactly what's going to happen afterwards, but we can be confident that things will be different.

Not everyone on HN knows about C++11 threading. A "hello world" is of interest to some people.

Concentration of measure. If you have a quantity that depends on a large number of random variables but not too strongly on any small subset of them, it tends to behave like a constant. That's the intuition behind the law of large numbers, the central limit theorem, a bunch of concentration inequalities, and model averaging.

Take a look at https://ocobook.cs.princeton.edu/OCObook.pdf, and in particular section 1.2. The online shortest path problem is a good example of an online optimization problem that's not related to machine learning.

This is nice to have. The classic reference on online learning is "Prediction, Learning and Games" but it's not ML focused and it predates pretty much everything on online convex optimization. I'm looking forward to seeing a more modern introduction.

1) Human height isn't actually bimodal even if some histograms display two modes. See https://amstat.tandfonline.com/doi/abs/10.1198/00031300265#.... for an analysis.

2) The central limit theorem applies to the distribution of the sample average. It applies whenever the samples are iid and the second moment is finite. The fact that the samples are coming from a mixture of normals doesn't change that.

A useful more advanced tip: don't put in writing that you're taking the conversation offline to avoid putting securities fraud in writing.

I had a terrible time in academia and I'll agree with everything that the OP wrote.

Not everyone starts a PhD right after undergrad. It'd be interesting to look at PhD students who started later in life and see how they take the program. Anecdotally, they have a lot of problems too.