Improved readability in the README.md file.

README.md

@@ -3,7 +3,8 @@ Generative Adversarial Networks
 
 This repository contains the code and hyperparameters for the paper:
 
-"Generative Adversarial Networks." Ian J. Goodfellow, Jean Pouget-Abadie,
+**Generative Adversarial Networks**. 
+Ian J. Goodfellow, Jean Pouget-Abadie,
 Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville,
 Yoshua Bengio. ArXiv 2014.
 
@@ -26,17 +27,17 @@ Moreover, we have not integrated any unit tests for this code into Theano
 or Pylearn2 so subsequent changes to those libraries may break the code
 in this repository. If you encounter problems with this code, you should
 make sure that you are using the development branch of Pylearn2 and Theano,
-and use "git checkout" to go to a commit from approximately June 9, 2014.
+and use **```git checkout```** to go to a commit from approximately June 9, 2014.
 
 This code itself requires no installation besides making sure that the
-"adversarial" directory is in a directory in your PYTHONPATH. If
-installed correctly, 'python -c "import adversarial"' will work. You
+```adversarial``` directory is in a directory in your ```PYTHONPATH```. If
+installed correctly, **```python -c "import adversarial```** will work. You
 must also install Pylearn2 and Pylearn2's dependencies (Theano, numpy,
 etc.)
 
-parzen_ll.py is the script used to estimate the log likelihood of the
+```parzen_ll.py``` is the script used to estimate the log likelihood of the
 model using the Parzen density technique.
 
-Call pylearn2/scripts/train.py on the various yaml files in this repository
+Call **```pylearn2/scripts/train.py```** on the various yaml files in this repository
 to train the model for each dataset reported in the paper. The names of
-*.yaml are fairly self-explanatory.
+```*.yaml``` are fairly self-explanatory.