Discover @sverneka Open Source projects

Sachin Vernekar (@sverneka)

sverneka

Stars
64
Global Rank 286,771 (Top 10 %)
Followers 5
Following 1
Registered about 7 years ago
Most used languages

Python
36.8 %

Jupyter Notebook
21.1 %

Java
15.8 %

C++
15.8 %

R
5.3 %

Lua
5.3 %
Location 🇧🇪 Belgium
Country Total Rank 1,379
Country Ranking

Jupyter Notebook
77

Lua
110

R
159

C++
302

Java
562

Python
680

OODGen

Out-of-distribution Detection via Generation - NeurIPS 2019

Jupyter Notebook

qrsPeakDetection

A Novel Approach for Robust Detection of Heart Beats in Multimodal Data

opengan

Comparison of open-set recognition using GAN and other methods.

ConfidentClassifierICLR19

Code for ICLR 2019 SafeML workshop paper: Analysis of Confident-Classifiers for Out-of-distribution Detection

Jupyter Notebook

tvseg

GSoC - TV show segmentation

Jupyter Notebook

DistanceCamera

This app computes distance of birds/objects using camera

wide-resnet-cifar100-kaggle

Wide-Resnet-Ensemble implementation for CIFAR100 kaggle submission: >83.97% on kaggle private leaderboard

FlavoursOfPhysics

Binary classifier to Identify a rare decay phenomenon

flappy-bird-weights

GpsArea

Android app to find area of the closed region traversed

researchPapers

Research Papers

BOOKING_HACK_A_HOLIDAY

Hack A Holiday Contest on Hackerrank

NationalBowl

Classify plankton images into 121 classes

DateYourData

2 class classifier to predict if a person is going to be admitted for a particular internship

WALMART_TRIP_TYPE

classify walmart customer visit into one of 38 trip types

MicrosoftChallenge

Classify 500GB of malwares into 9 classes

SpringLeafChallenge

2 class classifier to check if it is worth sending a promotional email to customer

projectilePathTrace

Linear regression and LSTM implementation to trace trajectory of a projectile.

ICLR2020

Jupyter Notebook

Fiduccia_Mattheyses_Algorithm_Implemntation

Fiduccia Mattheyses Algorithm Implemntation in C++

pcgClassify

A Novel Approach for Classification of Normal/Abnormal Phonocardiogram Recordings using Temporal Signal Analysis and Machine Learning