sourangshughosh Goto Github PK

chrisedignall.github.io

coronavirus-simulation

Simulating coronavirus with the SIR model

coronavirus_cure_deeplearning

Using deep learning to generate novel molecules as candidates for binding with coronavirus protease

covid-19

Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE

covid-19_model

The superiority and inferiority ranking method (or SIR method) is a multi-criteria decision making model (MCDA) which can handle real data and provides six different preference structures for the system user.

covid19_sirmodel2

Simulating coronavirus with the SIR model

deep-voice-conversion

What if you could imitate a famous celebrity's voice or sing like a famous singer? This project started with a goal to convert someone's voice to a specific target voice. So called, it's voice style transfer. We worked on this project that aims to convert someone's voice to a famous English actress Kate Winslet's voice. We implemented a deep neural networks to achieve that and more than 2 hours of audio book sentences read by Kate Winslet are used as a dataset.

docs

KOSS Documentation | A handbook of guidelines, principles and archives

doubly-stochastic-deep-gaussian-process

Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to over-fitting, and provide well-calibrated predictive uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalisations of GPs, but inference in these models has proved challenging. Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice. We present a doubly stochastic variational inference algorithm, which does not force independence between layers. With our method of inference we demonstrate that a DGP model can be used effectively on data ranging in size from hundreds to a billion points. We provide strong empirical evidence that our inference scheme for DGPs works well in practice in both classification and regression.

evolutionary-deep-neural-network

This is a software code for Evolutionary Deep Neural Network. Evolutionary Deep Neural Networks train neural networks using an evolutionary algorithm

excalidraw

Sketch hand-drawn like diagrams

finiteelementanalysis

A Simple Finite Element Method program

fluid-simulation-for-computer-graphics

A PIC/FLIP fluid simulation based on the methods found in Robert Bridson's "Fluid Simulation for Computer Graphics"

form

First Order Reliability Methods. Taylor series approximation of the performance function of different stochastic variables.

fuzzycmean

Fuzzy clustering is a form of clustering in which each data point can belong to more than one cluster. Clustering or cluster analysis involves assigning data points to clusters such that items in the same cluster are as similar as possible, while items belonging to different clusters are as dissimilar as possible

gaussianprocessmotionplanner

Gaussian Process Motion Planner

geneticprogramming-in-c

Genetic Programming in C++

hypre

Parallel solvers for sparse linear systems featuring multigrid methods.

jigsaw-solver

A jigsaw puzzle solver for randomly shuffled square shaped images.

kossiitkgp.github.io-1

Website of KOSS

latex-resume

My resume in latex. Fork and edit to make your own resume.

machinelearning_predicting_adverse_drug_reactions

The objective of this work by Sourangshu Ghosh was to develop machine learning methods that could accurately predict adverse drug reactions using the SIDER and OFFSIDEs databases.

markovchaintest

Python utility that uses a Markov Chain to generate random sentences using a source text

n-gram

An n-gram model is a type of probabilistic language model for predicting the next item in such a sequence in the form of a (n − 1)–order Markov model.

neuralturingmachine

This repository contains a stable, successful Tensorflow implementation of a Neural Turing Machine which has been tested on the Copy, Repeat Copy and Associative Recall tasks from the original paper.

olympiad

Olympiad in Informatics solutions

optimization_bayesian.jl

Bayesian optimization is a method of finding the maximum of expensive cost functions. Bayesian optimization employs the Bayesian technique of setting a prior over the objective function and combining it with evidence to get a posterior function. This permits a utility-based selection of the next observation to make on the objective function, which must take into account both exploration (sampling from areas of high uncertainty) and exploitation (sampling areas likely to offer improvement over the current best observation).

or_lab

Assignment Solutions of OR_Lab IIT KGP

or_lab-1

Operations Research Lab. Involves coding the various Linear Programming Problem optimization methods in C/C++.

physics742notes2020

Shared lecture notes for physics 742