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i said almost - that's code for "obviously i'm not talking about pathological regularizers"
Well, in that case minimizing the (negative) loglikelihood seems principled but you could minimize literally any loss function and it would work almost just as well.
I appreciate the lack of math notation, for many with a poor mathematics backgrounds it feels like a huge wall into getting into interesting and useful theories.
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Bayes Theorem hardly requires any math notation at all. It would literally take you less than a minute to understand conditional probability. Yikes.
Possibly true, but just looking at the Wikipedia page for Bayes Theorem, more than half the text on the page is math notation: https://en.wikipedia.org/wiki/Bayes%27_theorem It doesn't matter how simple the math actually is, if someone is unfamiliar with mathematical notation it's going to be overwhelming to read.
This is a bit like complaining about the existence of books because you never learned how to read.
change my mind: bayes in practice is just a way to regularize your model and the language of bayes makes it seem principled but really you could use literally any regularizer and it would work almost just as well. i believe this because ultimately you're always going to minimize loglikelihood anyway (and so the prior becomes the regularization term).
Counterpoint: regularization is just a way of specifying a Bayesian prior for maximum a posteriori estimation.
This is a far gentler introduction, and the rest of the blog is pretty good too: https://www.countbayesie.com/blog/2016/5/1/a-guide-to-bayesi... Edit: this is a different take on the subject but an enjoyable and accessible read too: http://mbmlbook.com/toc.html