From 726b62f304b1adfe33c769202ebb56c898b06e92 Mon Sep 17 00:00:00 2001
From: Harry Stuart <42882697+HStuart18@users.noreply.github.com>
Date: Wed, 1 Jan 2020 15:17:14 +1100
Subject: [PATCH] Update README.md

---
 README.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 508573d..c410e12 100644
--- a/README.md
+++ b/README.md
@@ -6,8 +6,9 @@ Since the inception of generative adversarial networks, I have been fascinated b
 
 Music generation has many different and exciting potential applications such as:
 
-Providing melody inspiration to artists
-Creating infinite, unique and free music without the need for audio file storage (for retail shops, restaurants, cafes, video games, radio stations etc.)
+- Providing melody inspiration to artists
+- Creating infinite, unique and free music without the need for audio file storage (for retail shops, restaurants, cafes, video games, radio stations etc.)
+
 GANs are already well-established in the image-processing domain, but not so much in NLP or audio-processing due to their sequential structure. After some investigaton, I learned about WaveGAN. So, I set out to adapt WaveGAN for piano in Tensorflow 2.0 using WGAN-GP as my training mechanism (as recommended by the paper).
 
 ## What it does