vigneshn1997 Goto Github PK

alembic

⚗️ A Jekyll boilerplate theme designed to be a starting point for any Jekyll website

anydbc_c_code

An anytime algorithm to find clusters given points in space(referring to paper-AnyDBC: An Efficient Anytime Density-based Clustering Algorithm for Very Large Complex Datasets)

brick

A declarative Unix terminal UI library written in Haskell

brickbreaker

cerebro-system

Data System for Optimized Deep Learning Model Selection

codenames-haskell

gstudio

gstudio is a node description framework (NDF) to create knowledge networks. We are recreating gnowsys-studio with mongodb as storage. This scaled up version with support for multiple projects will be used for the several OER and open courseware projects.

k-mediods-spark

night-canteen-data-analysis

Given a dataset comprising of all transactions that have happened All Night Canteen, analysed the data using data mining techniques and also draw inferences on the dataset. Different questions were also proposed which can be answered using data mining techniques.

ps1-codes

reverie

:art: A ridiculously elegant Jekyll theme.

rumble

⛈️ RumbleDB 1.17.0 "Cacao tree" 🌳 for Apache Spark | Run queries on your large-scale, messy JSON-like data (JSON, text, CSV, Parquet, ROOT, AVRO, SVM...) | No install required (just a jar to download) | Declarative Machine Learning and more

searching-in-semi-ordered-matrix

Given a semi-ordered matrix M of numbers designed a PRAM algorithm for finding (the position of) a given number K in M.

shared-nearest-neighbour-clustering

Implemented a Shared Nearest Neighbour Clustering algorithm in Scheme.

sieve-of-eratosthenes-parallel-algorithm

Implementation of Sieve of Eratosthenes Algorithm in MPI

toy-compiler-in-c

Compiler Project for the course Compiler Construction(CS F363) BITS Pilani

unionfind

Simulating parallel implementations of union find sequentially to develop an optimized union find algorithm

vigneshn1997.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

word-document-frequency-pram-algorithm

Given a file system and a root directory on Unix, traverse the tree and (i) extracted words from each text file, (ii) computed the document frequency (DF)() of each word (the number of documents (i.e. files) in which that word occurs), and (iii) determined the words with the K highest DF.

word-document-index-application

Given a large collection of documents distributed in the secondary memories (i.e. hard disks) of multiple nodes in a cluster, designed a program that extracts words and their frequency of occurrence from each document and create a word-document index in each node ranked on the frequency.