Rishabh Gupta (mr-easy)

mr-easy

User data from Github https://github.com/mr-easy

Location:India

Home Page:https://mr-easy.github.io/

GitHub:@mr-easy


Organizations
MLLabIISc

Rishabh Gupta's repositories

streaming_indicators

A python library for computing technical analysis indicators on streaming data.

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GMM-EM-Python

Python implementation of EM algorithm for GMM. And visualization for 2D case.

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Gibbs-Sampling-Visualized

Visualization of Gibbs sampling for 2D Gaussian distribution

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Badminton-Stroke-Classification

Classifying badminton strokes based on accelorometer and gyroscope sensor data attached to player's wrist. An end-to-end Machine Learning project, from data collection and preprocessing to final model evaluation.

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Restricted-Boltzmann-Machine

Python implementation of Restricted Boltzmann Machine (RBM). And an example on MNIST dataset.

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charLSTM

Pytorch implementation of character level LSTM for generating text, trained on Mark Twain's books.

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CodeChef-Solutions

Solutions to some awesome problems from codechef.

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K-Means

A simple visualization of K-Means algorithm in 2D

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SpaceDrift

An android game created in Android Studio.

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cs228-notes

Course notes for CS228: Probabilistic Graphical Models.

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DL_Project_1

Deep Learning Course Project - 1. FizzBuzz Program.

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DL_Project_2

Deep Learning Project 2

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DL_Project_3

Deep Learning Project 3

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mr-easy.github.io

My personal website.

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awesome-community-detection

A curated list of community detection research papers with implementations.

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bayesian-methods-for-ml

My solutions to Coursera hosted Bayesian methods course. (https://www.coursera.org/learn/bayesian-methods-in-machine-learning)

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chargrid-pytorch

Pytorch Implementation of Chargrid Paper (https://arxiv.org/abs/1809.08799)

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CLRS

:notebook:Solutions to Introduction to Algorithms

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implemetation_algorithms

Here is implementation of all basic algorithms in computer science.

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markdown

A Python implementation of John Gruber’s Markdown with Extension support.

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pdfjs-dist

Generic build of PDF.js library.

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PyTorch-CycleGAN

A clean and readable Pytorch implementation of CycleGAN

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svinet

This package implements algorithms for identifying overlapping communities in large undirected networks. The sampling based algorithms derive from stochastic variational inference under the (assortative) mixed-membership stochastic blockmodel. For details see the following reference: http://www.pnas.org/content/early/2013/08/14/1221839110.full.pdf

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Tagging

Build-Tool to automatic increase the version and create a tag in the remote VCS repository.

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three.js

JavaScript 3D library.

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