Just looking at employers in my professional career. Two. One for 15 years then the current for 3.
Looking at my direct and diagonal leaders, they seem to average 3-5 years a role, and I consider staying with my prior employer for so long a mistake. I made career progression and promotions there, but it still slowed me down vs changing employers.
Sure, self-hosting is a great option for very large projects, but a random python library to help with an analytics workflow isn’t going to self-host. Those projects, along with 27,999,990 others have chosen GitHub, often times explicitly to reduce the barrier to contribution.
Also, all of those examples are built on thousands of other FOSS projects, 99% of which aren’t self-hosting. This is the same as arguing only Amazon is a bookseller and ignoring the thousands of independent book publishers creating the books Amazon is selling.
Yep, we’re looking at that exact option right now. 6 free to see if it’s going to work then it’s time to max that deductible!
Yeah, model training is hard. Like capital H HARD. you need a bunch of data and it needs to be high quality.
New York is the financial center of USA, so separating finance jobs from job postings written by someone using New England vernacular is a step you need to go through to make sure your data is high enough quality.
So if you are just starting, use 20 newsgroups dataset in those links, it’s pretty good data with a ton of resources written about it. It’s not fun data, but it isn’t as likely to fall victim to biases in data you aren’t expecting.
Couple of options to start out with, Topic Labeling and Topic Extraction.
Topic Labeling is a classic example of supervised learning, or using ML with training data to classify new observations based on patterns found in training data.
Topic Extraction is a classic example of unsupervised learning, or attempting to identify patterns without training data.
I’m going to start with labeling, or classification here. There are plenty of tools to train a model to classify text in to categories, I’d recommend starting with this scikit-learn tutorial to see what’s involved before you start.
With any classification problem, you need good training data. You mentioned you’ve scraped 400 job postings, and I’m assuming you would want to using the job description to predict the job title. Some quick math, you’ll want to withhold 30% of your data to test your model, so that leaves 280 postings to train. I would recommend at least 100 descriptions per job title, so if you have 2-3 job titles, perfect, you’re ready to follow that tutorial with your own data!
If you have more that that, you probably won’t be able to do labeling/classification here, and will instead want to do topic extraction, where you’ll throw your walls of text at the machine and let the machine tell you the patterns it finds.
Topic modeling with spaCy and sci-kit learn is a great overview of this process, and plugging your own data in is pretty straightforward.
Both of these examples don’t even really scratch the surface of what’s possible with text based ML these days, but are perfectly viable tools to run quickly and on commodity hardware.
And the same people are still in charge, so…
Our friendly neighborhood screech owls used to meet up on our neighbors basketball goal the summer of 2020. Was a great thing to look forward to every night at dusk, so we built an owl box for them that squirrels have taken over.
I thought the millennial aspirations were a bit extreme, but as a millennial I get it. We had the Great Recession, outrageous prices for college, home prices are out of control.
And I say this as a millennial doing well. We don’t even think about money day to day or paycheck to paycheck, and are saving enough to largely minimize or potentially mitigate our kids needing student loans. But I am still strategically thinking about money and what will happen when the next recession or financial calamity hits, or hyper-inflation wiping us out.
The cost to live has been trying to outrun our income our entire adult lives, so sure, fuck it, double our income then maybe we have a chance to sleep at night even when it’s going well.
This looks phenomenal and I need to try it. Any recipe or link or even a guide on how to make this?
Looks like there is an open issue for this in GitHub. #602
It appears there’s successful reproduction now within the last 48 hours.
Your friend is paying it today in insurance premiums. Same money, just a different line on the paycheck.
And then if he gets sick, he’ll pay again.
Our willingness in America to pay money to a company that is incentivized by profits, just to not pay taxes is astonishing.
Below the elite level, relative skill differences can be large enough that a skilled cis women can outcompete a lesser skilled cis men. And that’s where 99% of sports are played so these rules/laws just serve to make cis men not feel threatened by potentially losing in a softball game to a woman.
At the more elite levels, though, the skill gaps are much smaller, and being faster or stronger are the difference. Most WNBA players can’t dunk, most NBA players can. Elite men run 100M a full second faster than elite women. At those levels, men have a distinct physical advantage.
There have been some studies indicating trans women still have higher lung capacity than cis women, more strength etc, but there’s still some uncertainty because the number of studies are limited, and there’s even one study that indicated cis women may have an advantage over trans women.
But considering the laws currently being passed, they aren’t targeting elite athletes, and are instead targeting kids, and not out of the spirit of competition, but out of hate.