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Tagged with bayesian-deep-learning probability
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How can supervised learning be viewed as a conditional probability of the labels given the inputs?
In the literature and textbooks, one often sees supervised learning expressed as a conditional probability, e.g.,
$$\rho(\vec{y}|\vec{x},\vec{\theta})$$
where $\vec{\theta}$ denotes a learned set of ...
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