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docs/src/models/losses.md

@@ -1,12 +1,23 @@
 ## Loss Functions
 Flux provides a large number of common loss functions used for training machine learning models. 
 
+Loss functions for supervised learning typically expect as inputs a target `y`, and a prediction `ŷ`.
+In Flux's convention, the order of the arguments is the following
+```julia
+loss(ŷ, y)
+```
+
 Most loss functions in Flux have an optional argument `agg`, denoting the type of aggregation performed over the
 batch: 
 ```julia
-loss(ŷ, y; agg=mean)
+loss(ŷ, y)                         # defaults to `mean`
+loss(ŷ, y, agg=sum)                # use `sum` for reduction
+loss(ŷ, y, agg=x->sum(x, dims=2))  # partial reduction
+loss(ŷ, y, agg=x->mean(w .* x))    # weighted mean
+loss(ŷ, y, agg=identity)           # no aggregation. 
 ```
 
+### Losses Reference
 ```@docs
 Flux.mae
 Flux.mse
@@ -22,4 +33,4 @@ Flux.hinge
 Flux.squared_hinge
 Flux.dice_coeff_loss
 Flux.tversky_loss
-```
+```