elliott34

amazing!

Spotify doesn't pay artists. They pay the labels. And labels pay the artists. What artists get paid has nothing to do with Spotify.

I have a lot of experience with this problem. The simplest way is via data munging in python/ pandas etc by finding what percent users convert/churn after doing an event N times within the first X days, and all the permutations thereof, using statistical tests around the change point. A more clever way is to use bayesian change point analysis.

The tricky thing is that these insights wind up being kind of obvious from the first analysis. You will find things like "users who use the software more are more likely convert." Other times these types of analysis will confirm what you already know. The tricky thing is making sure you have the right tagging/events and place to make sure you're getting at the right level of detail to get something worthwhile. It's very a much a garbage in garbage out type of thing.

Because the author draws a lot of parallels between computer science majors and physics majors, citing that physics majors are better prepared for the type of work ML requires. I am a physics major turned data scientist, and my argument is that I would have better prepared having been a CS major, given what the majority of my work requires. While in a CS major, a lot of data structures and algorithms haven't changed in 50 years either, you're much more likely to take electives with marketable skills or with up-to-date technologies (distributed systems, operating systems, OO/fp, databases, concurrency) that would've helped me in my day to day more than a math course or too did during my physics degree.

Doesn't really seem like the author spoke to a lot of actual physicists to write this article. Don't get me wrong, physics majors attract a certain type of intellect, but the vast majority of curriculum (quantum, EM, mechanics) are things THAT HAVE BARELY CHANGED IN THE PAST 50 YEARS. Meanwhile, CS majors come out much more prepared and hirable on the job market.

As far as the machine learning market goes, 90% of the projects require software engineering skills, the last 10% requires being able to go underneath the covers of linear algebra libraries, etc.

I just think the whole physics>cs degree for machine learning argument is not totally persuasive given my experience.

And this is thanks to Yann LeCun whose vast experience at Bell Labs experience has shown him how mixing engineer/business requirements/deadlines with research produces shitty results, and so he designed it this way.

2001: A Space Odyssey

Sunshine

Blade Runner, gattaca, the matrix, alien etc.etc. basically just google "best sci fi movies of all time"

Disagree about motivation. See Steven Pressfield's "War of Art" and "Do the work."

Motivation is ephemeral. Hard work, habit, consistently applying yourself regardless of your motivation, is key.

Not dangerous, in my experience. It doesn't take deep theoretical knowledge to provide value (i.e. value to a customer or to a business) through machine learning. Assuming here that one knows how to cross validate, check for overfitting, etc., and not shoot themselves in the foot.

EDIT: Note that it's still EXCEPTIONALLY difficult to provide true, lasting value to a given organization with this stuff and takes years of experience (note I didn't mention deep knowledge and experience with the latest techniques aboard the hype train).

I wish everyone on r/MachineLearning and those preparing for data science careers would read this comment and heed the advice of those of us who have actually spent time on data science teams and have experienced all of this first hand.

Would love to see this post if you have it handy.

I read Murakami books not because they are entirely entertaining WHILE I'm reading them, but because the dream-like memory of the experience sticks in your mind for years.