MayanAstronaut

Good catch, fixing it right now.

Much like search engines combat black hat SEO, amazon and reviews systems can do the same.

It would not be to hard too for amazon. Use reviews that are a 'Verified Purchase' and highly 'helpful' reviews as a training/test set. Then machine learn a weighting to every review based on the (product, reviewer, other reviews, etc features...)

The outcome could actually hinder tactics like this for products that game the system much like how link farms hurt sites they were propping up when Google figured out how to stop sites from gaming a search engine.

True. The mapping should be in github so people could add all case variations within reason.

I'd personally add (unicorn pivot|Unicorn pivot)--> "Stinking just made BS" on to it.

Not a good comparison.

At least gevent monkey patch all the flask and django for a async comparison.

Can you make a fourth comparison with this line added "from gevent import monkey; monkey.patch_all()" before the apps are init?

Thanks.

That's like saying it was not the Confederacy's fault for trying to stop the Thirteenth Amendment because it ruined their 'business model' too.

If a business pays money to make anyone's life harder then they are bad.

True, it comparisons do a lot better with more features.

This paper looks to just show the major winning aspect of using CovNets as they do not need many features as the deep net learns its own representations of the training data. It more to show CovNets work on more then just vision.

But architeching the pooling layers IS adding complex to the simple input feature set. Therefore the comparison should be of only state of the art ML.

Very true, every stat has issues.

Take payroll of population that this CEO thinks is a better metric. It totally ignores demographics of a aging population.

Baby-boomers are retiring, how a metric based CEO ignores this shows were the real bias is.

Looks great but memory usage is ambiguous. What if I load a data structure to over the 1gb allocation?

If I need to load a 1.2gb dictionary the whole thing topple?

Then whats the point of the voting system?

Also, many of the reviews are from users without any history.

Apps are already shady, add to this a promise to donate money and you get a down right scam feel from this.