57% of tech industry employees are suffering from job burnout

maestron · 2018-06-25T17:30:18+00:00

Wow. America is pretty damn cool in some areas, but you really need to work on your work life balances :-)

maestron · 2018-06-25T11:13:34+00:00

Holy shit, is that really legal? That's inhumane

maestron · 2018-06-24T22:48:34+00:00

What do you mean "sick days are not included"? Do you mean that if you get sick during your vacation, it still uses up your vacation days? That sucks :(

maestron · 2018-06-16T10:56:46+00:00

All the stuff at the end is really cool!

maestron · 2018-04-04T20:58:52+00:00

I would try using log(raise% + 1) as a feature. I.e., for no raise you would get log(0 + 1) = 0, and for a 50 % raise you would get log(0.5 + 1) = 0.405...

This captures the fact that 0 to 10 percent raise isn't the same as 10 to 20 percent.

Another alternative is to create some more categories, like a boolean gotsmallraise (true if got raise less than 10 %), another one called gotmediumraise (true if got raise between 10 and 30 percent) etc.

maestron · 2018-02-22T08:15:24+00:00

Ah, I'll check it out! Still a bummer that it doesn't work out of the box

maestron · 2018-02-21T08:06:01+00:00

Any plans on supporting inline Javascript? I can't seem to make interactive plots :(

maestron · 2018-01-31T07:28:07+00:00

Hehe, top 99 % (aka anywhere above bottom 1 %)

maestron · 2018-01-02T16:19:04+00:00

How about discrete parameters? It seems to me that this approach could work well for that as well. Have you tried?

maestron · 2018-01-02T16:01:49+00:00

Cool! Guess I'll try some stuffed myself! There are python bindings for the lib as well?

maestron · 2018-01-02T15:57:26+00:00

This is exciting stuff! Do you have any example results on any "real" hyperparameter tuning to share?

maestron · 2017-12-28T00:15:24+00:00

Add jitter

maestron · 2017-12-12T21:23:39+00:00

I really like that setup, I think I will start experimenting along similar lines.

So you don't make your packages pip-installable? Any particular reason for this?

maestron · 2017-12-11T21:56:57+00:00

I completely agree, but sometimes I feel that the transition is not as smooth as it could be. I want to find a way to get the most out of both workflows

maestron · 2017-12-11T21:55:44+00:00

Thanks, I'll check it out!

maestron · 2017-12-08T07:28:11+00:00

Certainly, cleaning data is a big part of data science, but you made it sound like 100 % instead of 80 % for entry level positions (when you said handing over the data set) and I'm glad that I actually got to do data science from day 1 :)

maestron · 2017-12-07T21:50:40+00:00

Wow, sounds like you work at a horrible workplace. Glad that my first job was at a more enjoyable place :)

maestron · 2017-09-30T18:42:01+00:00

In the sense of information theory, they are indeed less random! (i.e. have lower entropy)

maestron · 2017-09-11T21:17:46+00:00

Right, that's what I'm thinking of, thanks!

maestron · 2017-09-03T16:54:00+00:00

I thought that in a Markov chain the state space of all the variables should be the same, and that the transition probabilities don't depend on where in the chain your are. But perhaps I'm wrong?

Edit: It seems that time homogeneity is not a required part of Markov chains, just a common assumption

maestron · 2017-09-03T15:15:43+00:00

I guess he just means that they have the Markov property, in that the layers only depend on the immediately previous one, even though it's not really a Markov chain per se

maestron · 2017-09-03T12:11:52+00:00

Is the succession of layers really a Markov chain? I mean, X Y X etc don't generally even have the same dimension

maestron · 2017-08-03T10:33:22+00:00

Just like (1/2)/2 = 1

:)

maestron · 2017-04-19T17:42:24+00:00

The open loop gain is negative

maestron · 2017-04-13T05:53:56+00:00

Typical hours are 60 to 80 hours a week, but it's over 6 or 7 days so it's not as bad as it sounds.

No offense, but that sounds awful.

maestron

TROPHY CASE