LossFunctions.jl
LossFunctions.jl is a Julia package that provides efficient and well-tested implementations for a diverse set of loss functions that are commonly used in Machine Learning.
Available Losses
Distance-based (Regression) | Margin-based (Classification)
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Please consult the documentation for other losses.
Introduction
Typically, the loss functions we work with in Machine Learning
fall into the category of supervised losses. These are
multivariate functions of two variables, the true target y,
which represents the "ground truth" (i.e. correct answer), and
the predicted output ลท, which is what our model thinks the
truth is. A supervised loss function takes these two variables as
input and returns a value that quantifies how "bad" our
prediction is in comparison to the truth. In other words: