There are lectures that specifically use deep learning (in the form of fastAI's high-level `ColumnarData` API) to get state of the art results on the Rossmann Kaggle competition, but they _do_ borrow a good amount of feature engineering from the third-place winners from that competition.
It might very well be that the DNN sledgehammer is simply the wrong tool for the job in most structured-data cases; if that's so, even an article outlining _why not_ to use FastAI for these scenarios (with actual performance comparisons) would be nice.
That's all I'm looking for -- I should have mentioned that I am almost 100% new to ML/DL/data science & friends.
Comments
There are lectures that specifically use deep learning (in the form of fastAI's high-level `ColumnarData` API) to get state of the art results on the Rossmann Kaggle competition, but they _do_ borrow a good amount of feature engineering from the third-place winners from that competition.
It might very well be that the DNN sledgehammer is simply the wrong tool for the job in most structured-data cases; if that's so, even an article outlining _why not_ to use FastAI for these scenarios (with actual performance comparisons) would be nice.
That's all I'm looking for -- I should have mentioned that I am almost 100% new to ML/DL/data science & friends.