Update README - Jupyter Notebook is done

README.md

@@ -139,8 +139,6 @@ and use the most up-to-date versions of Miniconda and CUDA/cuDNN for your system
 
 #### Option 1: Jupyter Notebook
 
-Note: not yet added coming in couple of days :heart:
-
 Just run `jupyter notebook` from you Anaconda console and it will open the session in your default browser. <br/>
 Open `The Annotated GAT.ipynb` and you're ready to play!
 
@@ -257,8 +255,7 @@ Compare this to hardware needed even for the smallest of [transformers](https://
 
 ### Future todos:
 
-* Add jupyter notebook (coming soon)
-* Add inductive example
+* Add an inductive example
 * Potentially add an implementation leveraging PyTorch's `sparse API`
 
 The repo already has everything it needs for learning purposes, these are just the bonus points. I've tested everything

The Annotated GAT.ipynb

@@ -470,7 +470,9 @@
     "\n",
     "    plt.subplot(313)\n",
     "    plt.plot(hist, color='blue')\n",
-    "    plt.xlabel('node degree'); plt.ylabel('# nodes for a given out-degree'); plt.title(f'Node out-degree distribution for {dataset_name} dataset')\n",
+    "    plt.xlabel('node degree')\n",
+    "    plt.ylabel('# nodes for a given out-degree') \n",
+    "    plt.title(f'Node out-degree distribution for {dataset_name} dataset')\n",
     "    plt.xticks(np.arange(0, len(hist), 5.0))\n",
     "\n",
     "    plt.grid(True)\n",
@@ -13449,7 +13451,7 @@
     "\n",
     "We have the level 2 unlocked (the GAT model 🦄). 😍\n",
     "\n",
-    "And now, let's understand the model! 💪"
+    "And now, let's understand the model!"
    ]
   },
   {
@@ -13993,7 +13995,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now for the juice part. 🍪🎅\n",
+    "Now for the juicy part. 🍪🎅\n",
     "\n",
     "Here, we organize, high-level, everything we need for training GAT. Just combining the components we already learned."
    ]