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@@ -47,7 +47,7 @@ df = DeepFace.find(img_path = "img1.jpg", db_path = "C:/workspace/my_db")
 
 **Large Scale Face Recognition** - [`Demo with Elasticsearch`](https://youtu.be/i4GvuOmzKzo), [`Demo with Spotify Annoy`](https://youtu.be/Jpxm914o2xk)
 
-Notice that face recognition has O(n) time complexity and this might be problematic for millions level data. Herein, approximate nearest neighbor (a-nn) algorithm reduces the time complexity dramatically. [Spotify Annoy](https://sefiks.com/2020/09/16/large-scale-face-recognition-with-spotify-annoy/), [Facebook Faiss](https://sefiks.com/2020/09/17/large-scale-face-recognition-with-facebook-faiss/) and [NMSLIB](https://sefiks.com/2020/09/19/large-scale-face-recognition-with-nmslib/) are amazing a-nn libraries. Besides, [Elasticsearch](https://sefiks.com/2020/11/27/large-scale-face-recognition-with-elasticsearch/) wraps a-nn algorithm and it offers highly scalable feature. You should run deepface within those a-nn frameworks if you have really large scale data sets.
+Notice that face recognition has O(n) time complexity and this might be problematic for millions level data. Herein, approximate nearest neighbor (a-nn) algorithm reduces the time complexity dramatically. [Spotify Annoy](https://sefiks.com/2020/09/16/large-scale-face-recognition-with-spotify-annoy/), [Facebook Faiss](https://sefiks.com/2020/09/17/large-scale-face-recognition-with-facebook-faiss/) and [NMSLIB](https://sefiks.com/2020/09/19/large-scale-face-recognition-with-nmslib/) are amazing a-nn libraries. Besides, [Elasticsearch](https://sefiks.com/2020/11/27/large-scale-face-recognition-with-elasticsearch/) wraps an a-nn algorithm and it offers highly scalable feature. You should run deepface within those a-nn frameworks if you have really large scale data sets.
 
 **Face recognition models** - [`Demo`](https://youtu.be/i_MOwvhbLdI)