Kavita Ganesan’s Blog Posts

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AI for Business

15 Common AI Problem Types

One of the problems business leaders face in communicating with their technical counterparts is trying to describe their AI problem. To simplify some of the communication, here…

Technical Deep Dive for Practitioners

A Gentle Introduction to Deep Neural Networks with Python

This article examines the parts that make up neural networks and deep neural networks, as well as the fundamental different types of models (e.g. regression), their constituent parts (and how they contribute to model accuracy), and which…

FastText vs. Word2vec: A Quick Comparison

One of the questions that often comes up is what’s the difference between fastText and Word2Vec? Aren’t they both the same? Yes and no. They are conceptually the same, but there is a minor difference—fastText operates at a character…

Word2Vec: A Comparison Between CBOW, SkipGram & SkipGramSI

Word2Vec is a widely used word representation technique that uses neural networks under the hood. The resulting word representation or embeddings can be used to infer semantic similarity between words and phrases, expand queries, surface related concepts…

HashingVectorizer vs. CountVectorizer

Previously, we learned how to use CountVectorizer for text processing. In place of CountVectorizer, you also have the option of using HashingVectorizer. In this tutorial, we will learn how HashingVectorizer differs from CountVectorizer and when to use…

10+ Examples for Using CountVectorizer

Scikit-learn’s CountVectorizer is used to transform a corpora of text to a vector of term / token counts. It also provides the capability to preprocess your text data prior to generating the vector representation making it a…

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