Loss functions added to docs

docs/src/training/training.md

@@ -32,6 +32,18 @@ Flux.train!(loss, ps, data, opt)
 
 The objective will almost always be defined in terms of some *cost function* that measures the distance of the prediction `m(x)` from the target `y`. Flux has several of these built in, like `mse` for mean squared error or `crossentropy` for cross entropy loss, but you can calculate it however you want.
 
+In-built loss functions:
+```@docs
+mse
+crossentropy
+logitcrossentropy
+binarycrossentropy
+logitbinarycrossentropy
+kldivergence
+poisson
+hinge
+```
+
 ## Datasets
 
 The `data` argument provides a collection of data to train with (usually a set of inputs `x` and target outputs `y`). For example, here's a dummy data set with only one data point: