Deep Learning using Rectified Linear Units (ReLU)

Try ReLU at the End: A Simple Switch for Deep Learning

Deep learning usually uses a special math step at the end to pick a label, but you can also use ReLU — a simple rule that turns negative numbers into zero.
Instead of the usual soft choose step, this way takes the network's last signals, makes negatives zero, then picks the biggest number left as the answer.
The idea is simple, it keeps the model easy to read and can be fast in many cases.
You don't need extra smoothing functions; using ReLU at the end gives a direct, honest choice.
Teams who tried it noticed models still learn what they should and sometimes get clearer decisions.
It's not a miracle, its like trimming weak notes and letting the loudest note win.
For people curious how machines decide, this shows a different path for classification that feels plain and practical.
Small change, big thought for deep learning — give it a try and see what your models do, you might be surprised.

Read article comprehensive review in Paperium.net:
Deep Learning using Rectified Linear Units (ReLU)

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