Doc update (saving.md): removed outdated info; Typo fix.

Author: NightMachinery

CONTRIBUTING.md

@@ -8,7 +8,7 @@ One of the best ways to contribute is by looking at issues labeled ["help wanted
 
 ## Good First Issues
 
-While there are not many right now, we do have a section for ["good for issues"](https://github.com/FluxML/Flux.jl/labels/good%20first%20issue). As mentioned above, if any of these seem interesting but there is no clear next step in your mind, please feel free to ask for a suggested step. Often times in open source, issues labeled as "good first issue" actually take some back and forth between maintainers and contributors before the issues is ready to be tackled by a new contributor.
+While there are not many right now, we do have a section for ["good first issues"](https://github.com/FluxML/Flux.jl/labels/good%20first%20issue). As mentioned above, if any of these seem interesting but there is no clear next step in your mind, please feel free to ask for a suggested step. Often times in open source, issues labeled as "good first issue" actually take some back and forth between maintainers and contributors before the issues is ready to be tackled by a new contributor.
 
 ## Model Zoo
 

docs/src/saving.md

@@ -118,10 +118,4 @@ revert to an older copy of the model if it starts to overfit.
 @save "model-$(now()).bson" model loss = testloss()
 ```
 
-You can even store optimiser state alongside the model, to resume training
-exactly where you left off.
-
-```julia
-opt = ADAM()
-@save "model-$(now()).bson" model opt
-```
+Note that to resume a model's training, you might need to restore other stateful parts of your training loop. Possible examples are stateful optimizers (which usually utilize an `IdDict` to store their state, which is not automatically handled by `BSON`), and the randomness used to partition the original data into the training and validation sets.