update text

article.md

@@ -12,13 +12,13 @@ Files are converted to melspectrograms that perform better in general for visual
 
 Using With Fast.ai and three epochs with minimal lines of code approaches 95% accuracy with a 20% validation of the entire dataset of 8732 labelled sound excerpts of 10 classes shown above. Fast.ai was used to train this classifier with a Resnet34 vision learner with three epochs.
 
-epoch	train_loss	valid_loss	accuracy	time
-0	1.462791	0.710250	0.775487	01:12
-
-epoch	train_loss	valid_loss	accuracy	time
-0	0.600056	0.309964	0.892325	00:40
-1	0.260431	0.200901	0.945017	00:39
-2	0.090158	0.164748	0.950745	00:40
+| epoch	| train_loss |	valid_loss |	accuracy |	time |
+|-------|------------|-------------|-------------|-------|
+|0	| 1.462791 |	0.710250 |	0.775487 |	01:12 |
+| epoch |	train_loss |	valid_loss |	accuracy |	time |
+| 0	| 0.600056	| 0.309964 |	0.892325 |	00:40 |
+|1	| 0.260431	| 0.200901 |	0.945017 |	00:39 |
+| 2	| 0.090158 | 	0.164748 |	0.950745 |	00:40 |
 
 ## Classical Approaches
 
@@ -36,7 +36,7 @@ The state-of-the-art methods for audio classification approach this problem as a
 
 ## Credits 
 
-Thanks to [Kurian Benoy](https://kurianbenoy.com/) and countless others that generously leave code public. 
+Thanks to [Kurian Benoy](https://kurianbenoy.com/) and countless others that generously leave code in github to follow or write blogs that explain various things online. 
 
 ## Code Repo & Blog