diff --git a/README.md b/README.md
index c3ad84c..47562b7 100644
--- a/README.md
+++ b/README.md
@@ -39,7 +39,7 @@ dataset = [
 resp_obj = DeepFace.verify(dataset)
 ```
 
-Items of resp_obj might be unsorted when you pass multiple instances to verify function.
+Items of resp_obj might be unsorted when you pass multiple instances to verify function. Please check the item indexes in the response object.
 
 ## Face recognition models
 
@@ -72,7 +72,7 @@ DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", model = model)
 
 ## Similarity
 
-These models actually find the vector embeddings of faces. In other words, we use face recognition models as [`autoencoders`](https://sefiks.com/2018/03/23/convolutional-autoencoder-clustering-images-with-neural-networks/). Decision of verification is based on the distance between vectors. Distance could be found by different metrics such as [`Cosine Similarity`](https://sefiks.com/2018/08/13/cosine-similarity-in-machine-learning/), Euclidean Distance and L2 form. The default configuration finds the **cosine similarity**. You can alternatively set the similarity metric while verification as demostratred below.
+Face recognition models are regular [`convolutional neural networks`](https://sefiks.com/2018/03/23/convolutional-autoencoder-clustering-images-with-neural-networks/) and they are responsible to represent face photos as vectors. Decision of verification is based on the distance between vectors. We can classify pairs if its distance is less than a [`threshold`](https://sefiks.com/2020/05/22/fine-tuning-the-threshold-in-face-recognition/). Distance could be found by different metrics such as [`Cosine Similarity`](https://sefiks.com/2018/08/13/cosine-similarity-in-machine-learning/), Euclidean Distance and L2 form. The default configuration finds the **cosine similarity**. You can alternatively set the similarity metric while verification as demostratred below.
 
 ```python
 result = DeepFace.verify("img1.jpg", "img2.jpg", model_name = "VGG-Face", distance_metric = "cosine")