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Hi there šŸ‘‹ I'm Nabarun Barua. šŸ”­ Iā€™m currently working on NLP Projects.

My Technical Expertise is in -

  • Working in Deep Learning NLP
  • Working in Computer Vision


Languages and Tools:

HuggingFace Transformers python pytorch scikit_learn tensorflow docker DVC FastAPI flask git heroku linux mongodb mysql opencv postman bash

You can also find me on-

c17hawke | LinkedIn


nabarunbaruaAIML

nabarunbaruaAIML

Nabarun

Nabarun Barua's Projects

basicsr icon basicsr

Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, etc. Also support StyleGAN2, DFDNet.

easyocr icon easyocr

Ready-to-use OCR with 80+ supported languages and all popular writing scripts including Latin, Chinese, Arabic, Devanagari, Cyrillic and etc.

esrgan icon esrgan

ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR.

extendedtinyfaces icon extendedtinyfaces

Detecting and counting small objects - Analysis, review and application to counting

hybrid-compute-python-manage-vm icon hybrid-compute-python-manage-vm

Getting Started with Compute - Manage Virtual Machines using Python on Azure Stack hybrid cloud https://docs.microsoft.com/en-us/python/azure

kernl icon kernl

Kernl lets you run PyTorch transformer models several times faster on GPU with a single line of code, and is designed to be easily hackable.

nlp_cml_deployment_pipeline_on_kubernetes icon nlp_cml_deployment_pipeline_on_kubernetes

This is a continuation to the repository (https://github.com/nabarunbaruaAIML/CML_with_DVC_on_Transformer_NLP) with the deployment pipeline on EC2/Azure using Docker and Kubernetes.

olm-datasets icon olm-datasets

Pipeline for pulling and processing online language model pretraining data from the web

onnx_model_size_compression icon onnx_model_size_compression

Just a simple pythonic way of reducing the onnx converted model. This implementation is based on a tip by the Team ONNX .

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