Update readme

updated link to LeCun's paper

README.md

@@ -12,60 +12,37 @@ tags:
 - fiftyone
 - image
 - image-classification
-description: 'Curated MNIST dataset with train/val/test splits, including predictions
-  of different models: CLIP, LeNet, and retrained LeNet.'
+description: 'Curated MNIST dataset with train/val/test splits, including predictions of different models: CLIP, LeNet, and retrained LeNet.'
 split_field: tags
 dataset_name: curated-mnist
 organization: andandandand
 commit_message: Add curated MNIST dataset with train/val/test splits
-dataset_summary: '
-
-
-
+dataset_summary: |
   ![image/png](dataset_preview.webp)
 
-
-
   This is a [FiftyOne](https://github.com/voxel51/fiftyone) dataset with 70000 samples.
 
-
   ## Installation
 
-
-  If you haven''t already, install FiftyOne:
-
+  If you haven't already, install FiftyOne:
 
   ```bash
-
   pip install -U fiftyone
-
   ```
 
-
   ## Usage
 
-
   ```python
-
   import fiftyone as fo
-
   from fiftyone.utils.huggingface import load_from_hub
 
-
   # Load the dataset
-
-  # Note: other available arguments include ''max_samples'', etc
-
+  # Note: other available arguments include 'max_samples', etc
   dataset = load_from_hub("andandandand/curated-mnist")
 
-
   # Launch the App
-
   session = fo.launch_app(dataset)
-
   ```
-
-  '
 ---
 
 # Dataset Card for CuratedMNIST
@@ -122,9 +99,11 @@ The key additions include:
 
 - **Repository:** [andandandand/practical-computer-vision](https://github.com/andandandand/practical-computer-vision)
 - **Paper:**
-    - For MNIST: [Gradient-Based Learning Applied to Document Recognition](http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf)
+    - For MNIST: [Gradient-Based Learning Applied to Document Recognition](http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf)
     - For CLIP: [Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020)
-- **Demo:** [Image Classification and Dataset Curation with FiftyOne and PyTorch](https://colab.research.google.com/github/andandandand/practical-computer-vision/blob/main/notebooks/Image_Classification_and_Dataset_Curation_with_FiftyOne_and_PyTorch_Getting_Started.ipynb)
+- **Source:**
+    - [Notebook on GitHub](https://github.com/andandandand/practical-computer-vision/blob/main/notebooks/Image_Classification_and_Dataset_Curation_with_FiftyOne_and_PyTorch_Getting_Started.ipynb) 
+    - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/andandandand/practical-computer-vision/blob/main/notebooks/Image_Classification_and_Dataset_Curation_with_FiftyOne_and_PyTorch_Getting_Started.ipynb)
 
 
 ### Direct Use