Update README.md

README.md

@@ -22,9 +22,13 @@ Disclaimer: The model was fine-tuned after [Chapter 5](https://github.com/fastai
 
 The model was finetuned using the `cnn_learner` method of the fastai library suing a Resnet 34 backbone pretrained on the ImageNet dataset. The fastai library uses PyTorch for the undelying operations. `cnn_learner` automatically gets a pretrained model from a given architecture with a custom head that is suitable for the target data.
 
-Resnet34 is a 34 layer convolutional neural network. It takes residuals from each layer and uses them in the subsequent connected layers.
+Resnet34 is a 34 layer convolutional neural network. It takes residuals from each layer and uses them in the subsequent connected layers. Advantages of a resnet arquitecture ([Neurohive, 2019](https://neurohive.io/en/popular-networks/resnet/)):
+- Are easy to optimize, but the “plain” networks (that simply stack layers) shows higher training error when the depth increases.
+- Can easily gain accuracy from greatly increased depth, producing results which are better than previous networks.
 
-Specifically the model was obtained:
+ Please refer to the original paper '[Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf)' written by Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun.
+
+Specifically, the model was obtained:
 
 ```
 learn = cnn_learner(dls, resnet34, metrics=error_rate)