[mlthoughts2018]

The main reason though is that these other methods outperform neural nets in tons of different situations. Even just from an accuracy / business success metric point of view, many problems are just better solved with other classes of models, domain-specific feature engineering, etc. It will probably remain so for many decades at least.

[psandersen]

DNN's make good features though, especially if you have time series data or lots of text.

I agree that the final model should be a randomforest/xgboost/lightgbm for typical tabular data.

[mlthoughts2018]

I meant that extracting an intermediate layer as a feature embedding and then sticking a classical model on top of it performs worse than curating features through domain-specific expert tuning, for a ton of diverse application domains.