[docs] rewrite (#5175)

doc/source/using-ray-with-tensorflow.rst

@@ -4,26 +4,23 @@ Using Ray with TensorFlow
 This document describes best practices for using Ray with TensorFlow.
 
 To see more involved examples using TensorFlow, take a look at
-`A3C`_, `ResNet`_, `Policy Gradients`_, and `LBFGS`_.
+`A3C`_, `ResNet`_, and `LBFGS`_.
 
 .. _`A3C`: http://ray.readthedocs.io/en/latest/example-a3c.html
 .. _`ResNet`: http://ray.readthedocs.io/en/latest/example-resnet.html
-.. _`Policy Gradients`: http://ray.readthedocs.io/en/latest/example-policy-gradient.html
 .. _`LBFGS`: http://ray.readthedocs.io/en/latest/example-lbfgs.html
 
 
 If you are training a deep network in the distributed setting, you may need to
-ship your deep network between processes (or machines). For example, you may
-update your model on one machine and then use that model to compute a gradient
-on another machine. However, shipping the model is not always straightforward.
+ship your deep network between processes (or machines). However, shipping the model is not always straightforward.
 
-For example, a straightforward attempt to pickle a TensorFlow graph gives mixed
+A straightforward attempt to pickle a TensorFlow graph gives mixed
 results. Some examples fail, and some succeed (but produce very large strings).
 The results are similar with other pickling libraries as well.
 
 Furthermore, creating a TensorFlow graph can take tens of seconds, and so
 serializing a graph and recreating it in another process will be inefficient.
-The better solution is to create the same TensorFlow graph on each worker once
+The better solution is to replicate the same TensorFlow graph on each worker once
 at the beginning and then to ship only the weights between the workers.
 
 Suppose we have a simple network definition (this one is modified from the