[benhamner]

Both datasets you used (iris and digits) are way too simple for neural networks to shine.

Neural networks / deep neural networks work best in domains where the underlying data has a very rich, complex, and hierarchical structure (such as computer vision and speech recognition). Currently, training these models is both computationally expensive and fickle. Most state of the art research in this area is performed on GPU's and there are many tuneable parameters.

For most typical applied machine learning problems, especially on simpler datasets that fit in RAM, variants of ensembled decision trees (such as Random Forests) to perform at least as well as neural networks with less parameter tuning and far shorter training times.

[sine_dicendo]

Not for nothing but Ben did you read the article? He's not even discussing most of what you mention. He is simply taking his learning and applying it. You seem to be going off on a tangent about advanced applications where he is obviously just learning about how these things work and not trying to teach a method or suggesting that he has discovered anything significant..

To the author: I liked the article. A simple, concise read.

[dbecker]

In Ben's defense: The original article declares random forest a "winner" over neural networks. Ben's comment is a cautionary note that this result only applies to a specific class of problems.

This was a nice post, but it's reasonable to warn users not to overgeneralize the algorithm comparison.

[stiff]

He just shared some insights, he didn't critique anything and he's a Kaggler and published researcher so I don't get why he is getting downvoted.

[jph00]

Handwritten digits actually is a pretty good domain for deep nets, and the poor performance achieved in the article's case is due to the implementation (it needs deeper net, convolutional layer, etc). In that case much better (99%+) results have been achieved by deep nets for digit recognition. In fact, Hinton (in his Coursera course) recommends this domain for studying deep nets, since it is so well understood.

(Ben I know you're aware of all this already, but I just wanted to clarify for those who aren't as on top of the research as you)