relevantmeemayhere t1_jcrotun wrote

Mm, not really.

Bootstrapping is used to determine the standard error of estimates using resampling. From here we can derive tools like confidence intervals, or other interval estimates.

Generally speaking you do not use the bootstrap to tweak the parameters of your model. You use cross validation to do so.


relevantmeemayhere t1_jbl3bxy wrote

I mean, there will never be true equality when women don’t have to sign their bodies over to selective service or get a bunch of rights stripped away. Or when we overly commoditize the attention and speech (and just general financial, and social support) around women’s rights and empowerment when the education gap, sentencing gap, and mental health/suicide/substance abuse/homeless gap is continuing to widen with women being the “favored”, which harms men and drives them towards shit like trumpism.

The solution to your problem is ironically empowering men more, as the motherhood gap does have measurable affects on say; one’s earning ability.


relevantmeemayhere t1_j9ki2x1 wrote

Because they are useful for some problems and not others, like every algorithm? Nowhere in my statement did I say they are monolithic in their use across all subdomains of ml

The statement was that deep learning is the only thing that works at scale. It’s not lol. Deep learning struggles in a lot of situations.