diff --git a/docs/src/training/training.md b/docs/src/training/training.md
index 47bda1f5..b42db7c9 100644
--- a/docs/src/training/training.md
+++ b/docs/src/training/training.md
@@ -110,3 +110,30 @@ cb = function ()
   accuracy() > 0.9 && Flux.stop()
 end
 ```
+
+## Custom Training loops
+
+The `Flux.train!` function can be very convenient, especially for simple problems.
+Its also very flexible with the use of callbacks.
+But for some problems its much cleaner to write your own custom training loop.
+An example follows that works similar to the default `Flux.train` but with no callbacks.
+You don't need callbacks if you just code the calls to your functions directly into the loop.
+E.g. in the places marked with comments.
+
+```
+function my_custom_train!(loss, ps, data, opt)
+  ps = Params(ps)
+  for d in data
+    gs = gradient(ps) do
+      training_loss = loss(d...)
+      # Insert what ever code you want here that needs Training loss, e.g. logging
+      return training_loss
+    end
+    # insert what ever code you want here that needs gradient
+    # E.g. logging with TensorBoardLogger.jl as histogram so you can see if it is becoming huge
+    update!(opt, ps, gs)
+    # Here you might like to check validation set accuracy, and break out to do early stopping
+  end
+end
+```
+You could simplify this further, for example by hard-coding in the loss function.