Carmine Paolino's Projects
Prometheus middleware for aiohttp
Appunti di Calcolo delle Probabilità e Statistica
Arduino Micro project to mod Fanatec ClubSport V1/V2 Pedals to replace the original Fanatec controller board
Web interface for browsing, search and filtering recent arxiv submissions
Banana ripeness classification using Neural Networks.
Highlights code fragments on boost.org
Python bindings for Compact Language Detector v3 (CLD3)
Badges for Google Cloud Build
Notifier images for Cloud Build, complete with build status filtering and Google Secret Manager integration
Cluster Headache Tracker is a free, open-source web application designed to help individuals suffering from cluster headaches track and manage their condition. By providing detailed logging, visual insights, and easy sharing with healthcare providers, this tool aims to improve the understanding and treatment of cluster headaches.
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
Start a data science project with modern tools
Cookiecutter template for a Python package. See https://github.com/audreyr/cookiecutter.
Customizable SVG map visualizations for the web in a single Javascript file using D3.js
My dotfiles
All credits goes to Iterative.ai and their blog post https://blog.dataversioncontrol.com/data-version-control-tutorial-9146715eda46
Because the Emacs defaults are not so great sometimes.
Asynchronous multipart form upload with progress in Node.js
The user-friendly command line shell.
Powerline prompt for Fish Shell in Fish Shell.
List or delete enrolled fingerprints on fingerprint readers with fprint
A Mac OS X framework and source code that make it easy to access data through Google Data APIs (SVN Mirror)
Music Genre Classification using Deep Learning
Deep Neural Network for Genre Recognition
A scraper for Google Scholar, written in Python
Cousin of KnoXplorer: Browse HBase through Apache Knox
The Hitchhiker's Guide to Data Science for Social Good
A CLI workflow for the administration of Mac applications distributed as binaries
Code for the paper Sigtia, S., & Dixon, S. (2014). Improved Music Feature Learning with Deep Neural Networks