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-During first lesson of Practical Deep Learning for Coders course, Jeremy had mentioned how using simple computer vision model by being a bit creative we can build a state of the art model to classify audio with same image classification model. I was curious on how I can train an music classifier, as I have never worked on audio data before.
+During first lesson of Practical Deep Learning for Coders course, Jeremy had mentioned how using simple computer vision model by being a bit creative we can build a state of the art model to classify audio with same image classification model. I was curious on how I can train an music classifier, as I have never worked on audio data problems before.
 
 
-[You can find how I trained this music genre classification using fast.ai](https://kurianbenoy.com/ml-blog/fastai/fastbook/2022/05/01/AudioCNNDemo.html).
+[You can find how I trained this music genre classification using fast.ai in this blogpost.](https://kurianbenoy.com/ml-blog/fastai/fastbook/2022/05/01/AudioCNNDemo.html).
 
 ## Dataset
 
@@ -27,19 +27,6 @@ Fast.ai was used to train this classifier with a ResNet50 vision learner for 10
 |9  |	1.364363 |	1.368887 |	0.456167 |	02:04 |
 
 
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-| epoch	| train_loss	| valid_loss	| error_rate	| time  |
-|-------|---------------|---------------|---------------|-------|
-| 0	| 1.358123	| 1.100139	| 0.288713	| 05:14 |
-| 1	| 1.129988	| 0.985213	| 0.260693	| 05:12 |
-| 2	| 0.964907	| 0.909715	| 0.241337	| 05:17 |
-| 3	| 0.804738	| 0.843515	| 0.222475	| 05:19 |
-| 4	| 0.638846	| 0.795957	| 0.205347	| 05:16 |
-| 5	| 0.475434	| 0.750069	| 0.192673	| 05:15 |
-| 6	| 0.345060	| 0.742432	| 0.185198	| 05:12 |
-| 7	| 0.247938	| 0.728758	| 0.177624	| 05:12 |
-| 8	| 0.214708	| 0.727486	| 0.177871	| 05:11 |
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 ## Examples
 
 The example images provided in the demo are from the validation data from Kaggle competition data, which was not used during training.