I definitely agree that you don't need to go deep into theory to be able to do useful things. But I think the bias-variance tradeoff is a very bad example of "useless theory". It's essentially just another name for overfitting/underfitting, which are approximately the most important ML concepts there are.
I would again argue, the natural progression for this concept would be:
1.) Trains classifier
2.) My train error was so low! Why is my validation error so high
2.) Googles -> Why is my classifier training error lower than my validation error
3.) Learns about overfitting
4.) learns about bias variance
Its always a natural progression. Reading about this stuff without encountering it means it usually doesnt stick, and really doesnt make that much sense.
If you already have concepts of training and validation error then you're already there. The risk is not realising you can't test on your training data, or more subtly that you can't tune hyperparameters on your test data.
True, but I guess it depends on the person. Was just trying to give HN a view of how I write code. I've found it to be faster, but I go in knowing I will be doing a ton of googling.
This is one of the very few (!) concepts you need to know to get practical with ML. Why not watch a few videos on the concepts before you begin? They are all using high-school math anyway.
