Well put, always consider the simpler analysis that leads to better understanding first.
Deep Learning Is Becoming Overused
Understanding the data is the first port of call
By Michael Grogan in TowardsDataScience
There is always a danger when any model is used in a black-box fashion to analyse data, and models of the deep learning family are no exception.
Don’t get me wrong — there are certainly occasions where a model such as a neural network can outperform more simplistic models — but there are plenty of examples where this is not the case.
To use an analogy — suppose you need to buy a vehicle of some sort for transportation purposes. Buying a truck is a worthwhile investment if you regularly need to transport large items across long distances. However, it is a blatant waste of money if you simply need to go to the local supermarket to pick up some milk. A car (or even a bicycle if you are climate-conscious) is sufficient to carry out the task in question.
Deep learning is starting to be used in the same way. We are starting to simply feed these models with the relevant data, assuming that performance will surpass that of simpler models. Moreover, this is often done without properly understanding the data in question; i.e. recognising that deep learning would not be necessary if one had an intuitive grasp of the data. ... '
No comments:
Post a Comment