In the last post, we talked about Topic Modeling or a way to identify several topics from a corpus of documents. The method used there was Latent Dirichlet Allocation or LDA. In this article, we're going to perform a similar task but through the unsupervised machine learning method of clustering. While the method is different, the outcome is several groups (or topics) of words related to each other. For this example, we will use the Wine Reviews dataset from Kaggle.

There are various situations where the need is to group In simple terms, attention mechanism can be thought of an similar texts into same buckets. We do not have enough additional layer somewhere in a network architecture which previous experience or knowledge to run a classification gives the deep learning model extra controlling parameters to algorithm on top of the available data. Clustering is the refine its learning by paying attention to different parts of the fundamental and intuitive solution to such problems.