I'm with the author until he says to encode extra information into the URL. If the docs and the site are built well, you should find everything you need while completely ignoring the URL structure.
The latter form clearly presents the possibility of jumping straight to a different version's foo docs, to the docs homepage, etc. I'd consider this a quality of good UX.
Can anyone acquainted with the hard sciences comment on whether this paper passes even a basic sniff test? The title of the paper and so many references to other psychiatry journals seem like potential red flags.
That's quite fluffy, ethereal "bad hire" criteria. Nothing about ability to get the job done, communication with the team, quality of deliverables, reliability, or really anything a reasonable person would use as objective criteria to identify a bad hire. I wish I could read between the lines and understand what the author is really saying here, because we occasionally see articles written like this. What is your magic criteria to be in cool club? Please just speak plainly!
I have two daughters and I would tell them to say forcefully "Get your hand off my waist/leg" so everyone can hear it, and then physically remove his hand from her body. Then it's her option whether or not to make it a police matter or stay at the event and blow it off, depending on how she felt about it.
You can have a female founders conference, whose purpose is to uplift women, and allow men to be there as well. True story: I once attended a "women in tech" style conference as a hiring manager looking for potential recruits, and I won a random drawing for a prize. It was a purse with a company logo on it. Yes, it felt pretty sexist that no one expected a man would be there.
I'm sorry you didn't like the prize, but is it such a stretch that prizes at a women-in-tech conference would be targeted at women in tech?
You might not realize it, but women face a similar issue all the time at conferences/events where "unisex" (really men's) cut t-shirts are given out. These t-shirts are generally quite flattering on men and not-so-flattering on women, so I'd argue they're the woman's equivalent of your purse.
I went to the Grace Hopper conference this year and it was very refreshing to see that every company that gave out t-shirts used female cuts, since those are t-shirts I'll actually wear!
>You might not realize it, but women face a similar issue all the time at conferences/events where "unisex" (really men's) cut t-shirts are given out. These t-shirts are generally quite flattering on men and not-so-flattering on women, so I'd argue they're the woman's equivalent of your purse.
Erm. This kind of makes a lot of assumptions about what 'flattering on women' means. Which of course begs the question about a lot of possible latent sexism at play in your response.
The sorts of t-shirts given out at conferences don't fit anyone. They're generic pieces of cloth that wear like drapes. Hanes Beefy-Ts. That you think they're appropriate for men but not women kinda speaks volumes. Drapes over pecs are fine, but not breasts? Why? What would make a women's t-shirt fit in a more 'flattering' manner? Tighter? More forming to curves on the waist and breasts? Looser, more baggy? Why would that particular fit be flattering?
Also why is the goal to have a 'flattering' t-shirt -- which implies impressing other people, being an object for everyone else's gaze -- rather than a t-shirt that you like? It's sexism against women if we don't provide t-shirts that are 'flattering' to the people looking at her?
They're designed it seems for a man who's 5'10 and 170lbs or something and then just scaled linearly in both X and Y. That's not how human beings scale, though.
As a guy who's 6'3 and a bit more torso than legs I feel like I would need to be at least 250-275lbs for an XL shirt (which I need to wear so it's long enough) to not look like a sail.
This isn't a problem unique to women, it's unique to anyone who's not a 5'10 170lbs man; the mannequin they designed the shirt for.
I don't think it's equivalent. Handing out T-shirts that are clearly labeled "unisex" suggests you are trying to accomodate both men and women. Maybe you could accuse someone of ignorance about how unisex T-shirts fit women (I never knew unisex didn't fit women), but I wouldn't accuse them of sexism, quite the opposite because they clearly intended to accomodate.
It happens outside government too. It happens anytime your higher-level boss tells you: "Yes, I'm on board! You're right, a system like that would help us tremendously in several ways! Please estimate the costs for it so I can get us the money in our budget meeting next month." Happens all the time in enterprise businesses and the newer methodologies are extremely difficult to use in this sort of situation. In this case, I need some specs up front, create dev estimates, all to ultimately deliver a rough dollar amount or sometimes a "not to exceed" number for my boss...
This sounds more like "We don't negotiate with customers." because even if the customer had been professional and polite about it, their policy is no refunds. Even considering the behavior of the customer, IgnitionDeck is getting some really bad PR for the sake of saving $300.
They don't mean anything in particular. The actual analysis is being done in a high-dimensional space, in which each post is represented by a high-dimensional vector of the form [0,0,1,0,...., 0,1,0]. The length of the vector is the total number of distinct words used across all blog posts (maybe something like 30,000), and each entry is either 0 or 1 depending on whether the corresponding word occurs in this post. All the distances and cluster centers are actually being computed in this 30000-dimensional space; the two-dimensional visualization is just for intuition.
If you're wondering how the author came up with the two-dimensional representation, the article doesn't say, but it's likely he used something like Principal Component Analysis (http://en.wikipedia.org/wiki/Principal_component_analysis). This is a standard technique for dimensionality reduction, meaning that it finds the "best" two-dimensional representation of the original 30,000-dimensional points, where "best" in this case means something like "preserves distances", so that points that were nearby in the original space are still relatively close in the low-dimensional representation.
The point of the graphs are as an example more than anything — in any real world solution it's not trivial to plot your documents (as representing documents with thousands of words in 1d, 2d or 3d is not trivial).
But if _you could_ represent your documents in 2d, you could plot them like I do here. And, the X-axis would represent 1 feature of your documents, and the Y-axis another.
Tl;dr don't focus on the axes — focus on the idea of being able to compare the relative distances between documents.
If we have x, y, z, i, j, and n then we'd have six dimensions.
If each word represents a dimension, any blog title with that word gets a 1 on that word dimension, or a 0 if does not.
You can then calculate the distance between these points considering all of the dimensions, although clearly it's going to be a little limited on binary inputs, since the square of the differences is always going to be 1 or 0. Like for 4 dimensions, sqrt( ( 0 - 1 )^2 + ( 0 - 0 )^2 + ( 0 - 0 )^2 + ( 1 - 0 )^2 ), which just always gets simplified to something like sqrt( 1 + 0 + 0 + 1 ), which is a little boring.
The binary values cause your data points to all be stacked directly on top of each other, which leads me to believe that using binary inputs is a less than ideal application for k-means. Just look at it in the 2d case, where you have either [0,1], [0,0], [1,0], or [1,1] for each data point. Not very hard to determine the clustering there... basically just doing an overly complex boolean expression.
Think of those graphs as being something like the rubber sheet analogy that's often used to explain general relativity - it's not a great analogy in all respects, but humans can't really visualize 4 dimensions and it captures the gist of the idea well enough. Machine learning models have a similar problem: In reality the data points in k-means clustering exist in some very high-dimensinal space that's impossible to draw. So for the sake of visual aids it's common to draw 2D plots where the only thing they're meant to suggest is that points that appear close together in the visual aid would also be fairly close to each other in the actual problem space.
The graphs are just illustrations. The actual graphs would be high-dimensional (one dimension or axis for each word in the total word set), and each axis would have only two ticks on it: 0 and 1. A post is mapped to the binary vector in this space whose coordinates are determined by the presence or absence of words in the post.
