- The Ultimate Guide To Different Word Embedding Techniques In NLP - Nov 10, 2021.
A machine can only understand numbers. As a result, converting text to numbers, called embedding text, is an actively researched topic. In this article, we review different word embedding techniques for converting text into vectors.
- Topic Modeling with BERT - Nov 3, 2020.
Leveraging BERT and TF-IDF to create easily interpretable topics.
- Content-Based Recommendation System using Word Embeddings - Aug 14, 2020.
This article explores how average Word2Vec and TF-IDF Word2Vec can be used to build a recommendation engine.
- An Introductory Guide to NLP for Data Scientists with 7 Common Techniques - Jan 9, 2020.
Data Scientists work with tons of data, and many times that data includes natural language text. This guide reviews 7 common techniques with code examples to introduce you the essentials of NLP, so you can begin performing analysis and building models from textual data.
- WTF is TF-IDF? - Aug 2, 2018.
Relevant words are not necessarily the most frequent words since stopwords like “the”, “of” or “a” tend to occur very often in many documents.
- Recommender Engine - Under The Hood - Feb 21, 2018.
We examine two main types of recommender systems: Content based and Collaborative filtering. Both have their pros and cons depending upon the context in which you want to use them.