Update README.md

README.md

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-title: Demo
+title: HybridModel-GradCAM
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-## Visual Interpretation of a Hybrid Model 
 
-Building a hybrid model with *EfficientNet* and *Swin Transformer*, we have tried to inspect the visual interpretations of a CNN and Transformer blocks of a hybrid model (CNN + Swin Transformer) with the GradCAM technique. As a result, it appears that the transformer blocks are capable of globally refining feature activation across the relevant object, as opposed to the CNN, which is more focused on operating locally. However, the approach that will be shown here, is experimental. The workflow probably can generate a more meaningful modeling approach. The model is trained on [tf_flowers](https://www.tensorflow.org/datasets/catalog/tf_flowers) dataset, a multi-class classification problem.
-
-![]('./Presentation2.png')
+<table class="tfo-notebook-buttons" align="center">
+    
+  <td>
+    <a target="_blank" href="https://colab.research.google.com/drive/1usxq9yhZthyapAnzFfFObQ7RjXPicAop?usp=sharing"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
+  </td>
+    
+  <td>
+    <a target="_blank" href="https://github.com/innat/Hybrid-EfficientNet-Swin-Transformer"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a>
+  </td>
+    
+   <td>
+    <a target="_blank" href="https://deepnote.com/workspace/mohammed-innat-36e929bc-ce23-4d95-9ddc-a9c6662eb7d6/project/Notebooks-32e94ef4-8ce0-4cc4-8042-49862519f3f2/%2F%5BDeepnote%5D_HENetSwinT.ipynb"><img src="https://bit.ly/3NYODoU" />Run in Deepnote</a>
+  </td>
+    
+   <td>
+    <a target="_blank" href="https://www.kaggle.com/code/ipythonx/tf-hybrid-efficientnet-swin-transformer-gradcam"><img src="https://bit.ly/3HqRDIf" />Run in Kaggle</a>
+   </td>
+    
+   <td>
+    <a target="_blank" href="https://github.com/tensorflow/similarity/blob/master/examples/unsupervised_hello_world.ipynb"><img src="https://bit.ly/3mOOoRo" />Try on Gradio</a>
+   </td>
+    
+</table>