[estro]

I believe your branding hypothesis is correct, and all of this hype around neural nets and machine learning is creating a startup ecosystem ripe for market correction. I've heard a million times that most machine learning problems are solvable by regression, and I expect to hear that a million times more.

[CuriouslyC]

Regression is great if your output is a linear function of your inputs. Neural networks don't have that limitation. For a long time people have assumed that linear functions are the rule and nonlinearity is the exception, but the fact that nonlinear predictors are trouncing linear ones in many domains seems to be proving that false.

As for deep neural networks, in my opinion they are just an ensemble of neural networks, and ensemble methods have been shown to produce best-in-class results almost regardless of the base learning algorithm.

[j7ake]

Regression beats neural networks when you care about your model parameters.

[fullshark]

The only real advantage linear modeling has is marginal effects can be determined easily. This matters for some business intelligence / decision making applications but if you want to build a model with as small errors as possible there's really no reason to restrict your model to be linear in the age of massive computing power.

[digitalzombie]

Well it's also easy to model. NN needs lots of data from what I've read.

Also the whole sport analytic prediction industry mostly uses linear regression... But next hype thing is medical data for them but they're super slow in adopting new thing. Industry seems boring if you're not into sport and into modeling in general not just linear regression.