optimiser clarity

docs/src/training/optimisers.md

@@ -48,13 +48,15 @@ For the update step, there's nothing whatsoever wrong with writing the loop abov
 ```julia
 opt = SGD([W, b], 0.1) # Gradient descent with learning rate 0.1
 
-opt()
+opt() # Carry out the update, modifying `W` and `b`.
 ```
 
 An optimiser takes a parameter list and returns a function that does the same thing as `update` above. We can pass either `opt` or `update` to our [training loop](training.md), which will then run the optimiser after every mini-batch of data.
 
 ## Optimiser Reference
 
+All optimisers return a function that, when called, will update the parameters passed to it.
+
 ```@docs
 SGD
 Momentum