add wrap up

Author: dnth

examples/dinov2_notebook.ipynb

@@ -1429,6 +1429,35 @@
    "source": [
     "print(\"Feature vector matrix dimensions\", feature_vec.shape)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f3f75164-a790-436f-9ff5-ae95927b7f20",
+   "metadata": {},
+   "source": [
+    "## Wrap up\n",
+    "\n",
+    "With the embeddings extracted, we'll leave it to you on how you'd use them. Find anomalies, duplicates, or visualize them, whatever comes to mind.\n",
+    "\n",
+    "Feel free to check out our notebooks on how to use fastdup to find issues in your visual datasets. In combination with DINOv2 models, this could significantly improve the quality of your image dataset!\n",
+    "\n",
+    "We recommend checking out -\n",
+    "\n",
+    "- [**Quick Dataset Analysis**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/quick-dataset-analysis.ipynb) - Learn how to quickly analyze a dataset for potential issues. Identify duplicates, outliers, dark/bright/blurry images, and cluster similar images with only a few lines of code.\n",
+    "\n",
+    "- [**Cleaning Image Dataset**](https://nbviewer.org/github/visual-layer/fastdup/blob/main/examples/cleaning-image-dataset.ipynb) -  Learn how to clean a dataset from broken images, duplicates, outliers, and identify dark/bright/blurry images.\n",
+    "\n",
+    "As usual, feedback is welcome! Drop by our [Slack channel](https://visualdatabase.slack.com/join/shared_invite/zt-19jaydbjn-lNDEDkgvSI1QwbTXSY6dlA#/shared-invite/email) if you have questions!\n",
+    "Happy learning 😀"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ab6f39a3-603e-4914-9a14-9c8ee148fd9c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {