adraper2 Goto Github PK

3dhandclassification

A repo for my Graduate Computer Vision I semester project

adraper2

adraper2.github.io

Portfolio Site built with ReactJS hosted on GitHub pages with a custom domain.

airflow

Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

aws-ssm-tools

Handy tools for AWS Systems Manager - ssm-session, ecs-session, ssm-ssh and ssm-tunnel

conda

OS-agnostic, system-level binary package manager and ecosystem

csc-431-hpc

C code written for CSC 431 - High Performance Computing

csc401-final-project

Analyzing my own Spotify Saved Music listening habits to detect oddities and shifts in mood

datasets

Misc. datasets

disc_bayesian

This is a repository for project 2 during my DISC REU research program at the University of Notre Dame. This project involved applying a bayesian point process model using a forestry dataset from the 1800s.

disc_chesapeake

Project 1 of the DISC REU at Notre Dame. It involves classifying sedge and grass plots within the Chesapeake Bay using satellite image processing.

dotfiles-public

My personal dotfiles

keyloggergui

Keylogger GUI for Graduate Operating Systems at ND

missingno

Missing data visualization module for Python.

monodepth2

[ICCV 2019] Monocular depth estimation from a single image

my-dotfiles

My dotfiles as of late.

noise_reduction_research-sts499

This is a Github repository for my explorative analysis of statistical methods for reducing noise in signals, or photos specifically.

pandas_exercises

Practice your pandas skills!

rust_intro

A repo for me to play around with rust and cargo

shields

Concise, consistent, and legible badges in SVG and raster format

spotitry

A python script that uses Spotipy to create a custom Spotify playlist based on a user's current "short term" listening habits.

stats-code

Various statistics scripts from my Fall semester 2018 classes. Most of these were created for fun, or to make filling in factor diagrams easier.

sts347-final

This was the final project for our STS 347 project where we were asked to create a statistics-related Shiny App within R that could be understood by both, statistical and non-statistical, audiences.