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followers: 5.0 following: 2.0 repos: 16.0 gists: 0.0

Name: Nipun Goyal

Type: User

Company: @CER-REC

Bio: Data Scientist, CER | Deep Learning, MLOps, NLP, Computer Vision | BERT, gensim, Keras, scikit-learn, spaCy, TensorFlow| Azure

Location: Calgary, Canada

Blog: https://www.cer-rec.gc.ca/

Hi there 👋

I'm Nipun Goyal, Data Scientist at Canada Energy Regulator (CER). I've studied Data Science and Machine Learning from University of British Columbia, based in Vancouver, Canada. My areas of interest include Machine Learning, Natural Language Processing and Neural Networks. I am currently working on OCR text extraction from British Columbia Flooding Images to create impactful geo-spatial dataset. Outside of work, I take keen interest in Formula 1, Snooker, House Music and Stock Markets & Finance.

Nipun Goyal's Projects

app-dashboard

bert

TensorFlow code and pre-trained models for BERT

bert-text-classification

Fine-tuning BERT model for text classification with TensorFlow and TensorFlow Hub

cpsc330

CPSC 330: Applied Machine Learning

deep-learning-keras-tf-tutorial

Learn deep learning with tensorflow2.0, keras and python through this comprehensive deep learning tutorial series. Learn deep learning from scratch. Deep learning series for beginners. Tensorflow tutorials, tensorflow 2.0 tutorial. deep learning tutorial python.

documeta-the-art-of-generating-metadata

This project showcase the application of LDA Topic Modelling and KMeans Clustering for extracting information from the PDF documents

full-stack-deep-learning-2022

ml-basics

Exercise notebooks for Machine Learning modules on Microsoft Learn

mortgage-approval-modeling

nipun-goyal

nipun-goyal.github.io

ocr-text-extraction

Extracting name of the entities (environmental consultants) from the cover page of PDF Documents

pcmr

project-deliverables-visualizations

pytorch-tutorials

This repo consists of PyTorch code based regression, classification and computer vision problems

residential-energy-consumption-prediction

Predicting the Residential Energy Usage across 113.6 million U.S. households using Machine Learning Algorithms (Regression and Ensemble)

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