charleslparker

Machine Learning Benchmarking: You’re Doing It Wrong

I’m not going to bury the lede: Most machine learning benchmarks are bad. And not just kinda-sorta nit-picky bad, but catastrophically

In my previous two posts in this series, I’ve essentially argued both sides of the same issue. In the first,

In the first in this series of posts, I discussed a bit about why deep learning isn’t fundamentally different from

Gary Marcus has emerged as one of deep learning’s chief skeptics. In a recent interview, and a slightly less recent

In this second post of our series, we’ll cover a use case example. Least squares linear regression is one of

The idea of model fusions is pretty simple: You combine the predictions of a bunch of separate classifiers into a

One click and you’re done, right? That’s the promise of OptiML and automated Machine Learning in general, and to some extent,

There’s something nice about all this media attention being given to the AI these days. It’s nice for one’s work

To wrap up this series of blog posts, we’re going to get a little bit deeper into the business of

Over our last few blog posts, we’ve gone through the various ways you can use BigML’s new Deepnet resource, via