message: fixing minor documentation nits
diff: @@ -20,7 +20,7 @@ on many GPUs on one machine. Essentially, we create copies of all variables in
  the model's layers on each device. We then use all-reduce to combine gradients
  across the devices before applying them to the variables to keep them in sync.
  * [`CollectiveAllReduceStrategy`](https://www.tensorflow.org/versions/master/api_docs/python/tf/contrib/distribute/CollectiveAllReduceStrategy):
  -This is a version of `MirroredStrategy` for multi-working training. It uses
  +This is a version of `MirroredStrategy` for multi-worker training. It uses
  a collective op to do all-reduce. This supports between-graph communication and
  synchronization, and delegates the specifics of the all-reduce implementation to
  the runtime (as opposed to encoding it in the graph). This allows it to perform
  @@ -31,8 +31,8 @@ fault-tolerance to allow training to continue when there is worker failure.
  * [`ParameterServerStrategy`](https://www.tensorflow.org/versions/master/api_docs/python/tf/contrib/distribute/ParameterServerStrategy):
  This strategy supports using parameter servers either for multi-GPU local
  training or asynchronous multi-machine training. When used to train locally,
  -variables are not mirrored, instead they placed on the CPU and operations are
  -replicated across all local GPUs. In a multi-machine setting, some are
  +variables are not mirrored, instead they are placed on the CPU and operations
  +are replicated across all local GPUs. In a multi-machine setting, some are
  designated as workers and some as parameter servers. Each variable is placed on
  one parameter server. Computation operations are replicated across all GPUs of
  the workers.
  