LossFunctions.jl
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Getting Started
Installation
Overview
Getting Help
Background and Motivation
Terminology
Definitions
Alternative Viewpoints
Working with Losses
Instantiating a Loss
Computing the Values
Computing the 1st Derivatives
Computing the 2nd Derivatives
Function Closures
Properties of a Loss
Efficient Sum and Mean
Average Modes
Unweighted Sum and Mean
Sum and Mean per Observation
Weighted Sum and Mean
Distance-based Losses
LPDistLoss
L1DistLoss
L2DistLoss
LogitDistLoss
HuberLoss
L1EpsilonInsLoss
L2EpsilonInsLoss
PeriodicLoss
QuantileLoss
Margin-based Losses
ZeroOneLoss
PerceptronLoss
L1HingeLoss
SmoothedL1HingeLoss
ModifiedHuberLoss
DWDMarginLoss
L2MarginLoss
L2HingeLoss
LogitMarginLoss
ExpLoss
SigmoidLoss
Developer Documentation
Abstract Superclasses
Shared Interface
Shared Interface
Regression vs Classification
Deviations from Literature
Writing Tests
Acknowledgements
References
LICENSE
LossFunctions.jl
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