achompas

23 ms·

181.
by achompas 10y ago
Yeah, this is a selling problem. It feels like you're far more likely to gain traction starting a data team than taking an IC-track DE role. It's easier for companies to justify $200k+ for your skillset in that case, even if it
182.
by achompas 10y ago
My official title is "Data Scientist" although I'm closer to the "ML Engineer" someone else mentions in a child comment. Frankly speaking, if your company doesn't need a data engineer, it won't hire one or
183.
by achompas 10y ago
I'm not sure I buy this. The API meant they effectively outsourced product development instead of developing that competency internally. It's no surprise that the company struggled to develop compelling features or products for ye
184.
by achompas 10y ago
Oh this is interesting. Do you know people who use PyMC extensively, especially in industry? I'm working on some empirical Bayes estimation nowadays, and it's not unrealistic to think I'll encounter an ugly posterior soon eno
185.
by achompas 10y ago
Did these technical criteria skew towards mid-level or senior? I've observed this as well, but I've observed a more even balance of junior candidates. If so, this could be a manifestation of the high attrition rates for experience
186.
by achompas 10y ago
> How is that any different than the OP suggesting that hostile work environments are the single cause of poor diversity? I'm baffled by how this could possibly be your impression of the article. The WSJ is reporting on a recruiting
187.
by achompas 10y ago
It's interesting that you have not addressed the high attrition rates for women who do enter tech. Is that also a pipeline problem? Should schools also teach women to tolerate hostile work environments?
188.
by achompas 10y ago
> It sounds like your assumption is that a hostile work environment must be the reason for the higher quit rate, and thus that fixing the pipeline wouldn't fix the issue. I'm not sure this is as ironclad as you're pretendi
189.
by achompas 10y ago
No, I don't think so. We don't know what the recruiting funnel looks like at FB, but it is entirely possible that the hiring process has been designed by a group of engineers who, through their homogenous demographics, are either
190.
by achompas 10y ago
Sorry, so you're suggesting the problem is that women don't study computer science? Hmm. [0] It sounds like you're chalking this up to the famous "pipeline problem." [1] This is empirically incorrect. In particular,
191.
by achompas 10y ago
These growth rates touted in newspapers etc. are always adjusted for inflation. Source: I worked on these forecasts for 2 years.
192.
by achompas 10y ago
And you're claiming it's weird because people learn this early on? Maybe that's where we disagree -- it's not as common to learn about the issues with R^2 as you might think.
193.
by achompas 10y ago
Glad you got an answer. :)
194.
by achompas 10y ago
Agh I forgot to mention this! Thank you, I'll add a note to my post.
195.
by achompas 10y ago
Inference, in the context of statistical analysis, refers to the task of understanding underlying properties of a distribution. This is the definition used by practitioners. Machine learning practitioners care more about computational perfo
196.
by achompas 10y ago
Rhetorical question: what's so special about the polynomial coefficients when fitting a model? This isn't addressed in an article about regularization, yet it's a key point.
197.
by achompas 10y ago
> In actual machine learning context, it is usually called data resynthesis or data hallucination. Your comment is the fourth-highest search result for "data resynthesis" on Google. These are not common phrases by any stretch.
198.
by achompas 10y ago
I take issue with a number of points in your post, as they don't characterize the differences in the disciplines properly. > There both about inference This is actually incorrect -- a large swath of machine learning algorithms (like
199.
by achompas 10y ago
OP's point (and it's valid) is that the author covers the regularization term but does not explain the task aside from the following throwaway line: > "The goal is to find the model that minimzes (sp) this loss function.&q
200.
by achompas 10y ago
Thanks for your comment. I note several references throughout the article, and they do a better job of explaining than I can in an HN comment. You should check them out! I'll try to explain briefly though. Fitting a model is an optimiz
201.
by achompas 10y ago
I actually wrote a blog post on regularization a few months ago. I think it covers some of the issues, like overfitting, glossed over in this post: http://acompa.net/on-regularization.html
202.
by achompas 11y ago
I mean, it's clearly available as a cheaper + faster alternative to the rMB. Buying the Air isn't a dumb choice by any stretch, a similarly-specced 11" Air (8GB RAM is the only difference) is $200 cheaper and more powerful th
203.
by achompas 11y ago
A number of commenters have astutely identified the change in shot distribution over Kobe's career, which is due to a combination of age (older wing players tend to drive less and shoot more as they get older or experience injuries) an
204.
by achompas 11y ago
Great catch. What you're observing is the effect of a new trend (or a nonstationary distribution, if you prefer) wrt. shooting tendencies, as it affects the coaching and strategy of one (star) player. Your suggestion that > [the] ex
205.
by achompas 11y ago
It depends on the role. For our team, IMO the difference in attention is epsilon, since we already see so many applicants who have a minimum required exposure to ML. Source: not the hiring manager but involved in reviewing candidates for a
206.
by achompas 11y ago
I'm in your same boat, and my response to both of us (I guess??) is that the iPad air is now $399 out-the-door, which is a damn steal for a device that'll last for many years.
207.
by achompas 11y ago
So every time a faster processor comes out, the MSRP should increase? First time this happens in a while, as far as I can recall. FWIW I agree with you, Apple is basically forcing you to pay for the iPad Air + memory upgrade (but worse!)
208.
by achompas 11y ago
No, I'm saying that someone like Donald Knuth is obligated , by the terms of his employment as a premier academic, to sit on the cutting edge of both mathematics and computer science. /u/dxbydt commented on this very well, s
209.
by achompas 11y ago
> If you're in this field, and consider yourself an "engineer" but your math sucks, go read up on all you can about mathematics and statistics, just like you did back when you were learning about programming, operating sys
210.
by achompas 11y ago
EDIT: I forgot about the prevalence of uninformed priors. Thanks HN!

More ›