A few more README updates (#102)

Author: nealwu

README.md

@@ -2,8 +2,8 @@ Minigo: A minimalist Go engine modeled after AlphaGo Zero, built on MuGo
 ==================================================
 
 This is a pure Python implementation of a neural-network based Go AI, using
-TensorFlow. While inspired by Deepmind's AlphaGo algorithm, this project is not
-a Deepmind project nor is it affiliated with the official AlphaGo project.
+TensorFlow. While inspired by DeepMind's AlphaGo algorithm, this project is not
+a DeepMind project nor is it affiliated with the official AlphaGo project.
 
 ### This is NOT an official version of AlphaGo ###
 
@@ -32,7 +32,7 @@ Goals of the Project
    Google Cloud Platform for establishing Reinforcement Learning pipelines on
    various hardware accelerators.
 
-2. Reproduce the methods of the original Deepmind AlphaGo papers as faithfully
+2. Reproduce the methods of the original DeepMind AlphaGo papers as faithfully
    as possible, through an open-source implementation and open-source pipeline
    tools.
 
@@ -45,15 +45,15 @@ understandable implementation that can benefit the community, even if that
 means our implementation is not as fast or efficient as possible.
 
 While this product might produce such a strong model, we hope to focus on the
-process.  Remember, getting there is half the fun :)
+process.  Remember, getting there is half the fun. :)
 
 We hope this project is an accessible way for interested developers to have
 access to a strong Go model with an easy-to-understand platform of python code
 available for extension, adaptation, etc.
 
-If you'd like to read about our experiences training models, see RESULTS.md
+If you'd like to read about our experiences training models, see [RESULTS.md](RESULTS.md).
 
-To see our guidelines for contributing, see CONTRIBUTING.md
+To see our guidelines for contributing, see [CONTRIBUTING.md](CONTRIBUTING.md).
 
 Getting Started
 ===============
@@ -277,9 +277,9 @@ This command takes multiple tfrecord.zz files (which will probably be KBs in siz
 and shuffles them into tfrecord.zz files that are ~100 MB in size.
 
 Gathering is done according to model numbers, so that games generated by
-one model stay together.  By default, `rl_loop.py` will use directories
+one model stay together.  By default, [rl_loop.py](rl_loop.py) will use directories
 specified by the environment variable `BUCKET_NAME`, set at the top of
-`rl_loop.py`
+[rl_loop.py](rl_loop.py).
 
 ```
 gs://$BUCKET_NAME/data/training_chunks/$MODEL_NAME-{chunk_number}.tfrecord.zz