clarify

docs/src/training/training.md

@@ -25,6 +25,9 @@ m = Chain(
 
 # Model loss function
 loss(x, y) = Flux.mse(m(x), y)
+
+# later
+Flux.train!(loss, data, opt)
 ```
 
 The loss 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 `logloss` for cross entropy loss, but you can calculate it however you want.