Understanding Sampling Methods (Visuals and Code) — Sampling is the process of selecting a subset(a predetermined number of observations) from a larger population. It’s a pretty common technique wherein, we run experiments and draw conclusions about the population, without the need of having to study the entire population. …

Summarizing approaches from 10 research papers for Unsupervised Keyword Extraction from Text — This blog lists out (All?) popular Unsupervised Keyword Extraction Algorithms in Natural Language Processing (NLP). Keywords refer to the important phrases/expressions that are representative of the underlying document. Keywords from a document can accurately describe the document’s content and can facilitate fast information processing. …

Research Paper Explanation — Background Keyword/Keyphrase extraction is the task of extracting important words that are relevant to the underlying document. It lets you to enable faster search over documents by indexing them as document alias and are even helpful in categorizing a given piece of text for these central topics. Now, these could be…

Code Generation using GPT-J — Text generation is the task of automatically generating text using a machine learning system. A good text generation system can make it really hard to distinguish between human and machine-written text pieces. Some of the popular real-world use cases where text generation systems can be seen in action are Machine…

Decoding NLP Library — Concise Concepts — Named-entity recognition is the task of identifying key information from the text and tagging them under the pre-defined set of elaborate and meaningful categories for a given domain. The usual way of going about thinking of such systems is to train a many-to-many model under supervised setting, where the input…

Machine Learning Research Paper Summary — BERTScore is an automatic evaluation metric used for testing the goodness of text generation systems. Unlike existing popular methods that compute token level syntactical similarity, BERTScore focuses on computing semantic similarity between tokens of reference and hypothesis. …

Unsupervised Clustering Techniques in Machine Learning (with Visual illustrations) — TL;DR: In this blog, we will look into some popular and important centroid-based clustering techniques. Here, we will primarily focus on the central concept, assumptions and limitations w.r.t algorithms like K-Means, K-medoid, and Bisecting K-Means clustering methods. K-Means Clustering I am pretty sure you guys have heard of this one a lot. It…

Jupyter Notebooks are ❤️️ — In this blog, I will discuss how you can use the ipywidgets module and build an interactive annotation tool in your very own Jupyter Notebooks. All of you who are currently working or have worked for any startup as Data scientists know the spectrum of work they are expected to…