Question: Given a general classification problem (e.g., identifying cats, dogs and people from images), is it better to use a one-hot representation with a loss such

Given a general classification problem (e.g., identifying cats, dogs and people from images), is it better to use a one-hot representation with a loss such as cross-entropy (CE) than to treat the problem as a regression problem with a loss such as mean-squared-error (MSE)

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