Reset redundant phrase.

Author: ucdmkt

rfcs/20200420-tfx-tuner-component.md

@@ -331,7 +331,7 @@ class TunerSpec(ComponentSpec):
   }
 ```
 
-The KerasTuner library allows users to config
+The KerasTuner library allows users to configure
 [`tf.distribute.Strategy`](https://www.tensorflow.org/tutorials/distribute/kerass)
 if they are using
 [`kerastuner.Tuner`](https://github.com/keras-team/keras-tuner/blob/1.0.0/kerastuner/engine/tuner.py)
@@ -340,15 +340,15 @@ training) is executed in a single worker, as such only single machine strategy
 is allowed. To support multi-worker distributed training, we need to be able to
 execute the trial (training) on different workers.
 
-At the time of writing, KerasTuner library can be used for parallel tuning with
-single machine `tf.distribute.Strategy`, e.g.,
+At the time of writing, the KerasTuner library can be used for parallel
+execution of trail with a single machine `tf.distribute.Strategy`, e.g.,
 [`MirroredStrategy`](https://www.tensorflow.org/api_docs/python/tf/distribute/MirroredStrategy),
 multi-worker strategy (distributed training for trial) support is on the
 [roadmap of KerasTuner](https://keras-team.github.io/keras-tuner/tutorials/distributed-tuning/#data-parallelism-with-tfdistribute).
-Note that cluster managment of multiple worker machines is part of neither
-the KerasTuner library, nor is planned to be.
+Note that cluster management of multiple worker machines is not part of the
+KerasTuner library.
 
-At the time of writing, TFX doesn’t have the ability to manage the multi-worker
+At the time of writing, TFX does not have the ability to manage the multi-worker
 cluster and the centralized optimization service. As a result parallel tuning or
 distributed training is not supported natively in TFX (local or on-prem). In
 the next section, we will discuss the integration for Google Cloud. Similar