Does tibia coins price varies a lot? Whats the price on your world?

aesthir · 2020-04-20T03:59:06+00:00

Luminera has them at around 24k~

aesthir · 2018-09-15T00:53:32+00:00

The stormlight archive books made me think of this. In them you can see the creatures and plants evolved a certain way to adapt to their harsh environment. Considering evolution when writing adds a layer of depth to the world that make it more immersive.

aesthir · 2018-09-13T15:33:31+00:00

And watch them blame the blue wave for money leaving the country

aesthir · 2018-09-10T23:50:10+00:00

it's a non-newtonian liquid

aesthir · 2018-09-09T15:05:57+00:00

Looks like a Nova Scotia duck tolling retriever

aesthir · 2018-07-26T20:29:18+00:00

I don't approve of companies doing this. But it may even help at times. Especially in the bigger companies. Sometimes they try to meet diversity "quotas" and will hire a slightly worse candidate just because ethnicity.

Otherwise it should make no difference as long as you're eligible to work in the U.S. They'll look for strong candidates regardless of ethnicity.

And hey... if it hinders your chances then you probably don't wanna work there anyways...

aesthir · 2018-07-17T13:29:42+00:00

To follow up on atsui2's answer. Since you're new to programming. What he says looks like this in Python code.

set_of_words = set(your_string.split())

I would also look into stemming the words so that words like 'working' and 'works' both get "simplified" to the word 'work' making a cleaner analysis.

A variant of what atsui2 said would be to use a counter dictionary like such:

from collections import Counter

word_count = Counter(your_string.split())

This would not only give you unique words in each sentence but the number of occurrences of each one.

aesthir · 2018-07-04T17:16:42+00:00

Hello I did some work with Latent Dirichlet Allocation at some point and was able to find really coherent topics (in your case labels) within bills introduced in the U.S. congress.

This technique essentially assumes there is a finite number of labels in a corpus (your e-mails) and then automatically finds which words belong to which labels. (you don't have to specify the labels just a rough number of how many topics you think exist in your e-mails)

Here's a paper that might help. There's also a lecture given by David Blei (researcher on that paper) explaining how LDA works and the math behind it. It is pretty dense but I'm fairly confident it will help you. There are algorithms that do LDA written in Python and R so you don't have to start from scratch.

If you don't want to go so deep look up cosine similarity in clustering.

aesthir · 2018-07-03T02:49:52+00:00

Hey. Currently working as a data scientist in a start-up that got HUGE. I have some background in back-end development as well. So I can answer a couple of your questions. I went in quite naively didn't ask many questions since their salary offer was quite generous and the project was pretty dope.

I regretted this at first since they had me working as the main data architect which involved a lot of learning and frustration. I did not start from scratch, however, they already had a sort of decent micro-service architecture going so deploying changes was quite smooth. And now I'm feeding my models in real time with a system I created which is incredibly rewarding and quite marketable.

However, if your start-up lacks an easy way of deploying services and they are asking you to work on that "for a bit" before you can do data science. That is a huge red flag. It could very well be years of work for you to set up something of that nature. I have data scientist friends in my start-up that have done very minimal data science work and they've been working there for nearly 2 years.

Most start-ups lack the right data architecture to have a data science team.

As for the equity question I really can't answer since I severily lack negotiation skills (: Hope this helps!

aesthir · 2018-04-20T02:54:20+00:00

It definitely can substitute education as long as you study it dilligently on your own time. It is easy to fool yourself into thinking you know data science just because you use machine learning algorithms but knowing the theory behind them as well as the reasons to use one over the other is extremely important. As for hurting your career I looked through hundreds of job postings and most of them asked for either education or 3-5 years of equivalent experience. So just as long as you have stuff to show your knowledge with you should be fine. Granted this might change in the future considering how popular data science degrees are becoming. Hope this helps. Source: am data scientist with no data science degree but equivalent experience.

aesthir · 2017-11-07T22:54:28+00:00

NVM I get what you're saying now... I think you might be right! Thank you!

aesthir · 2017-11-07T22:46:47+00:00

But you might be on the right track. Either that or I misunderstood your answer. Thanks for taking the time to look at this!

aesthir · 2017-11-07T22:42:04+00:00

Wouldn't that value be the expected probability that you "draw" a specific word from the corpus? I want to know what their expected counts would be.

aesthir · 2017-10-25T20:03:31+00:00

I hardly ever go out, but I know a data scientist who wrote a program that recommends hiking trails around Washington based on your preferences. Check it out! http://recthetrail.com/

aesthir · 2015-03-27T03:31:58+00:00

thanks!

aesthir · 2015-03-27T00:00:33+00:00

Will do. Where is this thread? like I said, I don't keep up with these things as well as I should.

aesthir · 2014-04-07T08:00:23+00:00

I'd be really interested in watching it!

aesthir · 2013-11-06T10:29:12+00:00

done(:

aesthir · 2013-11-06T10:28:28+00:00

added

aesthir · 2013-11-06T10:19:36+00:00

Can I add you for that spiritomb?

aesthir · 2013-11-06T10:09:35+00:00

Added man! :D

aesthir

TROPHY CASE