DDE-2/Basics-of-CNN.md

@@ -49,7 +49,7 @@ _C. Ates_
 - [4 Pre-Trained Models for Image Classification](https://www.analyticsvidhya.com/blog/2020/08/top-4-pre-trained-models-for-image-classification-with-python-code/)
 - [Transfer learning with TensorFlow](https://www.tensorflow.org/tutorials/images/transfer_learning_with_hub)
 
-### Additional notebooks on transfer learning
+### Additional notebooks on transfer learning -- Melanoma Case
 <br /><a href="https://keras.io/api/applications/">Available </a>models<br /><br /><a href="https://www.kaggle.com/franvaluch/easy-skin-cancer-detection-cnn-resnet-vgg16#VGG16">Notebook</a>: CNN+ResNet+VGG16<br /><br /><a href="https://www.kaggle.com/nxrprime/siim-d3-eda-augmentations-and-resnext#seven">Notebook</a>: ResNet<br /><br /><a href="https://www.kaggle.com/gokulzuzumaki/melonama-skin-cancerclassification-efficientnet">Notebook</a>: <span class="nc">EfficientNet<br /><br /><a href="https://www.kaggle.com/ibtesama/melanoma-classification-with-attention#Model-:-VGG16-with-Attention">Notebook</a>: VGG16 with Attention<br /><br /><a href="vgg19%20">Notebook</a>: VGG19<br /><br /><a href="https://www.kaggle.com/sukanthen/cnn-architectures-custom-and-transfer-learning#7)-NASNet">Notebook</a>: NASNet</span><br /><br /><a href="https://github.com/Masdevallia/3rd-place-kaggle-siim-isic-melanoma-classification">Code</a>: ensemble of 8 different models<br /><br />Extended <a href="https://www.kaggle.com/zainahmad/eda-melanoma-classification-using-tensorflow/notebook#model-creation-and-training">dataset </a>notebook<br />
 
 ## YOLO: You Only Look Once