97 karma · joined April 22, 2015
For notebooks in an ML pipeline, I find that data issues are usually where things fail. Being able to run code "up to" a certain cell and create plots is invaluable. Creating reports by creating a data frame and displaying it as a cell is also super-handy.
You say, "dial some logic in", which is begging the wrong question (in my experience, at least). The logic in ML is usually very strait forward. It's about the data coming into your process and how your models are interacting with it.
https://economicsfromthetopdown.com/2022/04/08/the-dunning-k...
If you are right, and oral exams become the best way to evaluate student learning, then I could see smaller private schools becoming more popular as a correlation.
This brings up questions like "where did that extra $50 come from", but maybe you get the point of the original comment now.
Imagine how dysfunctional the world would be if you could never loose money investing in something.
The word "beginner" does a disservice to the level you need to be at to start wadding into quantum stuff. At a minimum, you better not be a beginner of linear algebra!
There is, imo, no better way to discourage people than saying this stuff is for "beginners".
This context is useful if I get sidetracked/distracted by a coworker, so I can retrace why I was looking at a certain page.