message: Very minor edits to performance_guide.md
diff: @@ -203,8 +203,8 @@ bn = tf.contrib.layers.batch_norm(input_layer, fused=True, data_format='NCHW')
  ### RNN Performance
  -There are many ways to specify an RNN computation in Tensorflow and they have
  -have trade-offs with respect to model flexibility and performance. The
  +There are many ways to specify an RNN computation in TensorFlow and they have
  +trade-offs with respect to model flexibility and performance. The
  @{tf.nn.rnn_cell.BasicLSTMCell} should be considered a reference implementation
  and used only as a last resort when no other options will work.
  @@ -230,7 +230,7 @@ If you need to run one step of the RNN at a time, as might be the case in
  reinforcement learning with a recurrent policy, then you should use the
  @{tf.contrib.rnn.LSTMBlockCell} with your own environment interaction loop
  inside a @{tf.while_loop} construct. Running one step of the RNN at a time and
  -returning to python is possible but it will be slower.
  +returning to Python is possible, but it will be slower.
  On CPUs, mobile devices, and if @{tf.contrib.cudnn_rnn} is not available on
  your GPU, the fastest and most memory efficient option is
  