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.
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.
How is the notion that "the vast majority of curriculum (quantum, EM, mechanics) are things THAT HAVE BARELY CHANGED IN THE PAST 50 YEARS" relevant to this discussion?
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.
I think one of the only companies doing this right, and that has the resources to do this right, is facebook, as they seperate AI research and teams that are focused on putting these things into production (i.e. ML engineers vs Ml researchers). Trying to combine these two things into the same role is resulting in continued confusion and frustration. I like this Stitchfix article as an overview (http://multithreaded.stitchfix.com/blog/2016/03/16/engineers...)
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.
Definitely agree! But you need to find it in yourself a driving reason to put in the hard work.
Tell a depressed, unmotivated person with no goals to just "work hard" and see how far that gets you. There needs to be some reason to do something rather than nothing.
I'm not a doctor, but I think part of the advice for certain types of depression e.g. people who have let their home get so untidy that they are overwhelmed at the thought of ever getting it tidy again, and very similar to advice for breaking writer's block, is just to make a start, and let the small amounts of progress form a reinforcement to continue.
This is true, but it's almost never structured as "just do the work". If anything, it's the opposite framing: do a little bit, just this one isolated task, and see the progress you make.
Pressfield's "do the work" annoys me, because it undervalues the importance of energy and a goal. It's a great approach to make reliable progress and fight distraction, but it's badly inadequate to the task of creating activity in the first place.
Hard work and habit are far more ephemeral than motivation is. Motivation is at the core of all and any activity and anyone saying they do not have any motivation and are just working hard are manipulating semantics. Motivation is absolutely paramount to take care of to do anything because if you don't have it your hard work will instantly evaporate.
It's a very fundamental condition. You can't do away with it. Being low on energy doesn't mean you're not motivated.
Hard work, habits, etc., are extremely relative things from what I've seen, and it's still not well understood what they mean and how they work.
Can we please stop worshiping someone's conviction from a book?
BJ Fogg [1] has done a ton of research on habit formation. I successfully used some of it to form simple habits like daily exercise, house chores, etc. Still, you need a reason to get going. I want to drop my body fat to 12%. Sounds stupid, but keeps me going...
I have read both books. Couldn't stop asking "Why is he waging this daily "war" with the imaginary evil, instead of spending his life on something more enjoyable?" This guy is either insane or has a pretty big hairy "why".
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.
