Update README.md

README.md

@@ -33,15 +33,18 @@ attracted the documents to the dense area are the topic words.
 ### The Algorithm:
 
 **1. Create jointly embedded document and word vectors using [Doc2Vec](https://radimrehurek.com/gensim/models/doc2vec.html).**
-Documents will be placed to other similar documents and close to most distinguishing words. 
+>Documents will be placed to other similar documents and close to most distinguishing words.
+
 ![Joint Document and Word Embedding](images/doc_word_embedding.svg)
 
 **2. Create lower dimensional embedding of document vectors using [UMAP](https://github.com/lmcinnes/umap).**
-Document vectors in high dimensional space are very sparse, dimension reduction allows the discovery of dense areas. 
+>Document vectors in high dimensional space are very sparse, dimension reduction allows the discovery of dense areas.
+
 ![UMAP dimension reduced Documents](images/umap_docs.png)
 
 **3. Find dense areas of documents using [HDBSCAN](https://github.com/scikit-learn-contrib/hdbscan).**
-The colored areas are the dense areas of documents. Red points are outliers that do not belong to a specific topic.
+>The colored areas are the dense areas of documents. Red points are outliers that do not belong to a specific topic.
+
 ![HDBSCAN Document Clusters](images/hdbscan_docs.png)
 
 **4. For each dense area calculate centroid of document vectors in original dimension. (centroid = topic vector)**