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Lead Data Scientist Consultant || Ex Co-Founder & CTO of a funded AI startup

Question generation using state-of-the-art Natural Language Processing techniques

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Question answering is a very popular task in Natural language processing but question generation is novel and hasn’t been explored much yet.

If you want to try a live demo of question generation in action, please visit

Question generation has a lot of use cases with the most prominent one being the ability to generate quick assessments from any given content. It would help school teachers in generating worksheets from any given chapter quickly and decrease their work burden during Covid-19.

I along with two other awesome interns Parth Chokhra and Vaibhav Tiwari built an easy-to-use, open-source library to…

The most practical use of word embeddings (word2vec, glove, etc) you will ever see.

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King — Man + Woman = Queen

You might have seen the traditional word2vec or Glove word embeddings examples that show King -Man+Woman = Queen. Here Queen will be returned from the word embedding algorithm given the words King, Man, and Woman. Today we will see how we can use this structure to solve a real-world problem.

1. The problem definition:

Group semantically similar documents easily

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In semantic search, we search a database of documents for a given user query and get back a set of relevant documents.

Whether we are building a recommendation system or question answering system often times we get back duplicate search results that are not exactly identical to be called duplicates but are near-duplicates that we can merge.

Imagine shopping for shoes on an e-commerce store and you get back 10 search results for shoes and out of them half are near-duplicates of each other with just a change in the viewing angle. We want the ability to merge these near-duplicates…

Tips to market your AI course

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This year I launched my first ever online course on Udemy titled “Question generation using Natural Language processing” and made $3333 dollars in 5 months with 0 marketing spend and 100% paid enrollments.

A practical AI project using HuggingFace transformers and Gradio App

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Practical use case

Using Wordnet, Conceptnet, and Sense2vec algorithms to generate distractors

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What are distractors?

Distractors are the wrong answers in a multiple-choice question.

For example, if a given multiple choice question has the game Cricket as the correct answer then we need to generate wrong choices (distractors) like Football, Golf, Ultimate Frisbee, etc.

Minimalistic code for few-shot text generation with HuggingFace

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I’m sure most of you have heard about OpenAI’s GPT-3 and its insane text generation capabilities learning from only a few examples.

The concept of feeding a model with very little training data and making it learn to do a novel task is called Few-shot learning.

A website GPT-3 examples captures all the impressive applications of GPT-3 that the community has come up with, since its release. GPT-3 is shown to generate the whole Frontend code from just a text description of how a website looks like. It is shown to generate a complete marketing copy from just a small…

A Google Colab code notebook exploring a real-world use case with OpenAI CLIP

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Problem Statement:

Imagine that you are a writer and you are searching for the best image that goes with your blog or book. You have a search phrase in mind like “Tiger playing in the snow”. You go onto copyright-free image websites like Pixabay or Unsplash and try out various combinations of keywords like “Tiger”, “Snow”, “Tiger Snow” etc to find relevant images.

If you are lucky you find the exact image that you are looking for on the first page or in the top N retrieved results.

Since the images in these websites have only tags, you are limited by the…

A journey from 1500 to 16500 Linkedin followers

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1. A letter from the president of India — 900k+ views

Merchandise, Quotes as a service, and offline events.

Original Quotes by Author. Images from

It has been almost three years since I had quit my job in silicon valley, moved back home (India), and took a plunge into entrepreneurship.

The journey accompanied with its ups and downs had spawned a myriad of emotions and thoughts. Sometimes they transformed into what I can call motivational quotes.

Ramsri Goutham

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