SourangshuGhosh
I am a fourth-year undergraduate student of the department of civil engineering enrolled in its Dual Degree Program at Indian Institute of Technology Kharagpur
Repositories
Select a repository to view its commits, contributors, and more.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.
rough_surfaces
Computational mechanics for rough surfaces and fractures
AwesomeFortranLibraries
This is a collection of Fortran routines written by Sourangshu Ghosh over the years for use in more complex codes. The different files are as independent as possible from each other, but in some cases dependencies are unavoidable.
SeismicAnalyzer
SEISMIC_CPML is a set of sixteen open-source Fortran90 programs to solve the two-dimensional or three-dimensional isotropic or anisotropic elastic, viscoelastic or poroelastic wave equation using a finite-difference method with Convolutional or Auxiliary Perfectly Matched Layer (C-PML or ADE-PML) conditions
Stochastic_LatentDirchletAnalysis
Python implementation of Stochastic Variational Inference for LDA
BitCoinPricepredictor
Bitcoin price prediction algorithm using bayesian regression techniques
FiniteElementAnalysis
A Simple Finite Element Method program
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.
Steel_Sections_Properties
A Python software able to calculate the cross-section properties of combined steel sections
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.
100_ComputerScience_Papers
100 Computer Science Papers listed by Sourangshu Ghosh
CGP-CNN-Design
A Genetic Programming Approach to Designing CNN Architectures, In GECCO 2017 (oral presentation, Best Paper Award)