textminer

14 ms·

91.
by textminer 14y ago
Oh, now I have to yell at myself. No doubt Python's sorted is some n*logn quicksort, and not actually in quadratic time. Crow for all!
92.
by textminer 14y ago
The pedant in me squirms at seeing a quadratic-time solution to something so linear. Use a heap or a scan, sir!
93.
by textminer 14y ago
Best Kafka-short-as-an-HN-comment I've ever read. Won't be sleeping tonight.
94.
Making Cython Work with Machine Learning's Hash Trick(blog.newsle.com)
2 points by textminer 14y ago|0 comments
95.
by textminer 14y ago
I've wondered this, too! It helps that elementary functions are in some sense dense in the set of all other functions. Results like Stone-Weierstrass show that on a compact interval, any function can be arbitrarily-well approximated by a po
96.
by textminer 14y ago
You just made me realize something. I attributed my own college grades slipping a bit my senior year to burnout, but it was also Fall '08, and I had just got an iPhone 3G, my first smartphone...
97.
by textminer 14y ago
I agree with this. I find the convention most stifling-- my instinct in the afternoon is to go work somewhere else, but quickly guilt* starts to pop up and tell me I'm doing something wrong, even if it's just to do work more ably. (* - this
98.
by textminer 14y ago
I can remember times being a student, learning a lot, but losing focus of how amazing it was I had dedicated time solely to study. As someone working full-time now, who only gets to learn new fascinating math in his precious free time, I im
99.
by textminer 14y ago
I've noticed people taking note of and commenting on Kara Swisher's take-charge, arrogant, brusque nature far more often than with many male tech journalists. Unexamined bias, or an expectation that female correspondents be softer and nicer
100.
by textminer 14y ago
About computationally as complex, no? The Manhattan distance would just be the sum of the absolute difference in the two coordinates. The Manhattan and Euclidean distance on finite spaces both generalize to "l_p norms" (1 and 2, respectivel
101.
by textminer 14y ago
We walk blocks, not as the bird flies.
102.
Sparse Matrix/Vector Multiplication for Text Classification(blog.newsle.com)
3 points by textminer 14y ago|0 comments
103.
by textminer 14y ago
Not useful for Kevin here, but for those with fat, frozen Python dictionaries that're useful to some vital process (e.g., feature lookup), the Python wrapper for the MARISA trie is wonderful. Tend to save 80-90% of memory whenever I capture
104.
by textminer 14y ago
The rewards game for first employees is perplexing. You have an order of magnitude less incentive than the founders who birthed the thing. Arguably, that's taken care of: you're on a real salary while they're making peanuts. You're also cap
105.
by textminer 14y ago
Maddening how so many classic textbooks have new versions every few years that add a few nominal features ("Calculus in the Real World!" "Online Tutorials That're Worse Than What Someone Else Made on YouTube!") with the real goal of shuffli
106.
by textminer 14y ago
Like you, that also floors me. Moved here in mid-2011, fresh out of grad school. Seems to be cheap when there's not a dotcom bubble, yet then we wouldn't be here. Ain't that a kick in the head.
107.
by textminer 14y ago
In Oakland, I'd some nice parts of Lake Merrit, Chinatown and Temescal. Rockridge is nice, if thoroughly yuppified. Richmond's Marina Bay and El Cerrito are both thoroughly nice and BART-adjacent, if a bit too suburban and removed.
108.
by textminer 14y ago
Silicon Valley is stucco-corporate ugly. San Francisco is charming, yet now grossly-expensive. The two are linked by decrepit transport systems that are being supplanted only by Google/Yahoo/Facebook's own shuttles (services that do nothing
109.
by textminer 14y ago
R's not going to teach you about pre-measures, Carathéodory's theorem, or Radon-Nikodym. Break out the whiteboards! (Then bust out R again when you want to play with stochastic processes and Brownian motion.)
110.
by textminer 14y ago
"Pedigree Matters" as an untruth: I wish this were the case. Too many entrepreneurs I know are only entrepreneurs because they have the support system of comfortably wealthy families and the professional network gained at elite schools and
111.
by textminer 14y ago
Seemed unfortunately successful in its advertising. Have a brother in first grade who is a champion at using my iPhone and iPad to play games and look up dinosaur pictures and videos. Suggested to my stepmom he get an iPad mini or Nexus 7 f
112.
by textminer 14y ago
Incredibly interested in your work here. For small-dimensional problems (or problems with features that can be engineered to be small-dimensional), ensemble methods through random forests and bagging and the like are incredibly useful. But
113.
by textminer 14y ago
Love it. It's that way with range, too, right? Does one end up casting things to lists more often if so much is naturally an iterator?
114.
by textminer 14y ago
Love defaultdict. It and dict/set/list compressions are a big part of what makes Python so fast to write in. Great practice for 2.7 that's probably quashed in 3.0. For large dicts, no need to create a giant set en route when iterating over
115.
by textminer 14y ago
My first downvote! Thanks for the constructive feedback, stranger.
116.
by textminer 14y ago
Thanks for the advice. Honestly, being that I work in startups, am a year and a half out of grad school, don't have to well-to-do parents, and support my partner, I think you've helped me realize it's definitely not so smart to be bufferles
117.
by textminer 14y ago
I agree here. The solution for me on not quite getting concepts in calculus and engineering was to delve into greater abstraction. When you get linear algebra, you understand the inverse function theorem in analysis. When you understand Hil
118.
by textminer 14y ago
Would you change that suggestion if the fresh grad had near 100k in loan debt, some of which was private and high-interest? The benefit of 10k sitting in savings when it could pay off a savage Chase loan seem less great. (To be explicit, th
119.
by textminer 14y ago
Learning about the basis of logistic regression in Ng's Stanford class was eye-opening. Also like that he then motivated generalized linear models, and why they're nice (e.g., parameters are linear with input data; the maximum-likelihood hy
120.
by textminer 14y ago
(Pardon me/blame autocorrect: Breiman.)

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