Bal Narendra Sapa's repositories

road-detection

This is a course project for DSCI-6011 - Deep Learning. deals with Drivable Area and lane segmentation for self driving cars

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cybersecurity-ner

Named-Entity-Recognition for Cybersecurity. This repository contains code for fine-tuning a pretrained ner model with custom dataset

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faq-llm

This is course project for DSCI 6004 deals with fine-tuning a pretrained model llm with a custom data

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balnarendrasapa

This repo contains readme that appears on my profile.

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chatbot

A Simple chatbot using langchain and streamlit. This repo uses light weight falcon4b llm model

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colab_gpu

Using GPU with git Repos in Google Colaboratory

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doc-chatbot

Streamlit chatbot app that can answer questions based on a pdf. see the readme for more information

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education_impact_analysis

This GitHub repository contains the code and documentation for an analysis of the impact of education levels on individuals' performance scores. Using ANOVA, we compare score means across different education groups, revealing significant differences. For a comprehensive analysis and detailed results

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mask-detection

Mask Detection using Deep Learning. This repository contains code to train a model that can detect whether a person is wearing mask or not.

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denoiser

This repository is about denoising the images. In this case, MNIST dataset is taken. The model is built with pytorch with autoencoder architecture

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docker-compose-files

This repository contains useful docker-compose files. contains docker-compose files for mysql, postgres etc. use these files to instantly launch apps

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dsa

This repository contains answers to the problems from leetcode, codechef, hackerrank etc

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init.lua

my custom neovim configurations

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object-detection

This repository contains the app for object detection

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portfolio

This repo contains code for my portfolio site.

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project_api

Building this REST API for fun. This repo includes implementation in Django and FastAPI. See the Readme for more Information

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spam_classification

Identify and filter out spam emails using Logistic Regression with this project. Developed a model that leverages various email attributes, achieving an accuracy range of 90% to 95%. Explore our GitHub repository for comprehensive details on this email spam detection solution.

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