danielkunin Goto Github PK

bivariate-clt

Final project for Casey Dunn's "Interacting with Data" course at Brown University worked on with Tyler Devlin

brown-datathon-2017

Introduction to D3.js for the 2017 Brown Datathon.

coastalshindigs

cottageshindigs

deeplearning-visualizations

grasp

Code for "Picking Winning Tickets Before Training by Preserving Gradient Flow" https://openreview.net/pdf?id=SkgsACVKPH

hmm-stack

This is an updated version of the original HMM Stack Algorithm by Taehee Lee and Daniel Kunin.

icme-deep-learning

icme-statistics

message-in-a-bottle

CS229 final project on Information Bottleneck

molecular-dynamics-simulation

For my Applied Math senior capstone project I am creating a browser-based, dynamic, 3-dimensional simulation of organic molecules.

neural-mechanics

normal-distribution

personal-website

pokemon-po

A D3.js visualization of our naive algorithm and improved algorithm for catching Pokemon proposed for the 2016 Brown Mathematical Contest for Modeling.

regularized-linear-autoencoders

Loss Landscapes of Regularized Linear Autoencoders

rethinking-sgd

sail-blog-new-post

The repository for making new post submissions to the SAIL Blog

seda

Visualization of SEDA database.

seeing-theory

Seeing Theory is a project designed and created by Daniel Kunin with support from Brown University's Royce Fellowship Program and National Science Foundation group STATS4STEM. The goal of the project is to make statistics more accessible to a wider range of students through interactive visualizations.