Fixed typos.

ptn/README.md

 # Perspective Transformer Nets
 
 ## Introduction
-This is the TensorFlow implementation for the NIPS 2016 work
-["Perspective Transformer Nets: Learning Single-View 3D Object Reconstrution
-without 3D
-Supervision"](https://papers.nips.cc/paper/6206-perspective-transformer-nets-learning-single-view-3d-object-reconstruction-without-3d-supervision.pdf)
+This is the TensorFlow implementation for the NIPS 2016 work ["Perspective Transformer Nets: Learning Single-View 3D Object Reconstrution without 3D Supervision"](https://papers.nips.cc/paper/6206-perspective-transformer-nets-learning-single-view-3d-object-reconstruction-without-3d-supervision.pdf)
 
 Re-implemented by Xinchen Yan, Arkanath Pathak, Jasmine Hsu, Honglak Lee
 
-Reference: [Orginal implementation in
-Torch](https://github.com/xcyan/nips16_PTN)
+Reference: [Orginal implementation in Torch](https://github.com/xcyan/nips16_PTN)
 
 ## How to run this code
 
-This implementation is ready to be run locally or distributed across multiple tasks.
+This implementation is ready to be run locally or ["distributed across multiple machines/tasks"](https://www.tensorflow.org/deploy/distributed).
 You will need to set the task number flag for each task when running in a distributed fashion.
 
 ### Installation
@@ -25,7 +21,7 @@ You will need to set the task number flag for each task when running in a distri
     *   Alternately, Download bazel from
         [https://github.com/bazelbuild/bazel/releases](https://github.com/bazelbuild/bazel/releases)
         for your system configuration.
-    *   Check for the bazel version by typing: bazel version
+    *   Check for the bazel version using this command: bazel version
 *   matplotlib
     *   Follow the instructions [here](https://matplotlib.org/users/installing.html).
     *   You can use a package repository like pip.
@@ -46,9 +42,9 @@ This code requires the dataset to be in *tfrecords* format with the following fe
     *   Flattened list of voxels (float representations) for the object.
     *   This is needed for using vox loss and for prediction comparison.
 
-You can download the ShapeNet Dataset in tfrecords format the  from this [here](https://drive.google.com/file/d/0B12XukcbU7T7OHQ4MGh6d25qQlk)<sup>*</sup>.
+You can download the ShapeNet Dataset in tfrecords format from [here](https://drive.google.com/file/d/0B12XukcbU7T7OHQ4MGh6d25qQlk)<sup>*</sup>.
 
-<sup>*</sup> Disclaimer: This data is hosted personally by Arkanath Pathak for non-commercial research purposes. Please cite the [ShapeNet paper](https://arxiv.org/pdf/1512.03012.pdf) in your works when used for non-commercial research purposes.
+<sup>*</sup> Disclaimer: This data is hosted personally by Arkanath Pathak for non-commercial research purposes. Please cite the [ShapeNet paper](https://arxiv.org/pdf/1512.03012.pdf) in your works when using ShapeNet for non-commercial research purposes.
 
 ### Pretraining: pretrain_rotator.py for each RNN step
 $ bazel run -c opt :pretrain_rotator -- --step_size={} --init_model={}
@@ -66,7 +62,7 @@ To compare the visualizations make sure to set the model_name flag different for
 This code adds summaries for each loss. For instance, these are the losses we encountered in the distributed pretraining for ShapeNet Chair Dataset with 10 workers and 16 parameter servers:
 ![ShapeNet Chair Pretraining](https://drive.google.com/uc?export=view&id=0B12XukcbU7T7bWdlTjhzbGJVaWs "ShapeNet Chair Experiment Pretraining Losses")
 
-You can expect such images after fine running the training as "grid_vis" under **Image** summaries in TensorBoard:
+You can expect such images after fine tuning the training as "grid_vis" under **Image** summaries in TensorBoard:
 ![ShapeNet Chair experiments with projection weight of 1](https://drive.google.com/uc?export=view&id=0B12XukcbU7T7ZFV6aEVBSDdCMjQ "ShapeNet Chair Dataset Predictions")
 Here the third and fifth columns are the predicted masks and voxels respectively, alongside their ground truth values.