message: A few more README updates
diff: @@ -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
  