dpaul0501 Goto Github PK

anylabeling

Effortless AI-assisted data labeling with AI support from Segment Anything and YOLO!

bullyid

Bulyidapp

covid-chestxray-dataset

We are building an open database of COVID-19 cases with chest X-ray or CT images.

covid19model

Code for modelling estimated deaths and cases for COVID19.

cvpr2015

deepd-exploring-linguistic-pattern-learned-by-deep-tech-for-depression-detection

dharmicdata

Comprehensive Open-Source Collection of Hindu Sacred Texts including Ramcharitmanas, Bhagavad Gita and Rigveda for Educational and Research Purposes.

emnlp17-depression

fb_mle_week3

finetune-deploy-llm

flask-ml-pipeline_gcp-tutorial

from-0-to-research-scientist-resources-guide

Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.

hydejack

"Best Jekyll Theme by a Mile"

imageannotatorandprediction

irse_codebert

This repository contains the code and notebooks for IRSE submission

irse_fire_2022_submission

Repo for code submission pertaining to the FIRE IRSE Track

lightning-covid19

Classification for covid-19 chest X-ray images using Lightning

llmops-fourthbrain

ml-deployment-on-gcloud

A pipeline to CI/CD of a machine learning model on Google Cloud Run

mlflow-example

This repository provides an example of dataset preprocessing, GBRT (Gradient Boosted Regression Tree) model training and evaluation, model tuning and finally model serving (REST API) in a containerized environment using MLflow tracking, projects and models modules.

particleground-portfolio

A minimalistic particle theme landing page template. ⚛️

pneumonia-diagnosis-using-xrays-96-percent-recall

BEST SCORE ON KAGGLE SO FAR , EVEN BETTER THAN THE KAGGLE TEAM MEMBER WHO DID BEST SO FAR. The project is about diagnosing pneumonia from XRay images of lungs of a person using self laid convolutional neural network and tranfer learning via inceptionV3. The images were of size greater than 1000 pixels per dimension and the total dataset was tagged large and had a space of 1GB+ . My work includes self laid neural network which was repeatedly tuned for one of the best hyperparameters and used variety of utility function of keras like callbacks for learning rate and checkpointing. Could have augmented the image data for even better modelling but was short of RAM on kaggle kernel. Other metrics like precision , recall and f1 score using confusion matrix were taken off special care. The other part included a brief introduction of transfer learning via InceptionV3 and was tuned entirely rather than partially after loading the inceptionv3 weights for the maximum achieved accuracy on kaggle till date. This achieved even a higher precision than before.

research-course

Research "How To" Course from Prof. Feamster

sunistha

:checkered_flag: Learn how to deploy your web application to Heroku from scratch step-by-step in 7 minutes!

sunistha_profile

wedding-invitation

Animated website for my Elder Sister's wedding Invitation (also contains timer & background music) | Save the Date: Nov 29, 2020

wedding-website

Our Wedding Website 👫