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A wise man once told me that inexperienced engineers tend to undervalue simplicity. Since I’m not wise myself, I don’t know whether this is true, but the ideas which show up in many different contexts do seem to be very simple. This post is about two of the most widely used ideas in deep learning, the mean squared error loss and the cross entropy loss, and how in a certain sense…
A wise man once told me that inexperienced engineers tend to undervalue simplicity. Since I’m not wise myself, I don’t know whether this is true, but the ideas which show up in many different contexts do seem to be very simple. This post is about two of the most widely used ideas in deep learning, the mean squared error loss and the cross entropy loss, and how in a certain sense they’re the simplest possible approaches.
Let’s start with the mean squared error, which is used when you want to predict a continuous value.Read on /cross-entropy/ ↗
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