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Hi I'm Bhavesh !

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I'm a Computer Science Graduate student at the University of Ottawa, Canada. πŸ€–. I am a team player, flexible 🌐 and always open to new challenges πŸ’».


  • I live in Ottawa, Ontario, Canada.
  • I'm Pursuing Masters in Computer Science at the University of Ottawa (Thesis Based).
  • Currently I'am doing COOP at Nokia in the Cloud and Network Services Team.
  • I'm a Research Assistant at the Smart IOT lab Uottawa and working on predicting Corn yield on RGB and Multispecral Orthomosaics of farms based in Ottawa using Deep learning. The thesis is in collaboration with Agriculture Canada and Invest Ottawa Link
  • I'm a 2X Kaggle Expert. Link
  • I'm a former Senior Analyst with more than 2 years of experience working with large scale Machine learning, MLOps and AWS.
  • I play lead guitars for a metal band called Innuendo based in Ottawa

πŸ”¨ Languages and Tools:

pytorch

tensorflow

Python

Typescirpt

Linux



My GitHub Stats πŸ’»

Bhavesh's GitHub stats]

Bhavesh Singh Bisht's Projects

awesome-pytorch-list icon awesome-pytorch-list

A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

claff-happydb icon claff-happydb

Corpus and annotations for the CL-Aff Shared Task from the University of Pennsylvania

dall-e icon dall-e

PyTorch package for the discrete VAE used for DALLΒ·E.

distribued-computing-system-codes icon distribued-computing-system-codes

This repo contains code to understand the connection between client and server in a distributed computing framework. Repo also contains two election algorithms, namely Bully and Ring

face_liveness_detection-anti-spoofing icon face_liveness_detection-anti-spoofing

face liveness detection activate, the script asks the person to generate an action, for example one of the actions they may ask you to do is smile, turn your face to the right, get angry, blink, etc. The actions are requested randomly, after fulfilling all the actions it generates a message saying "liveness successful" or "liveness fail"

faiss icon faiss

A library for efficient similarity search and clustering of dense vectors.

future-sales-prediction icon future-sales-prediction

This a famous problem on Kaggle where we have to predicted monthly sales of different shops with different item_ids. Some of the important features I used were lagged features, holidays, mean encoding and creating combinations of item and shops on data block num level.

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