sshuster Goto Github PK

followers: 0.0 following: 0.0 repos: 1.4K gists: 6.0

Type: User

sshuster's Projects

pip_conda_demo

demo on how to create a pip and anaconda package from scratch

pix2vertex.pytorch

An official pyTorch port of the pix2vertex paper from ICCV2017

playableenvironments

Official PyTorch implementation of "Playable Environments: Video Manipulation in Space and Time", CVPR 2022

plot-digitizer

Get very accurate data points by extracting bezier curves from svg file

plot2spec

an automatic plot digitizer for spectroscopy images (i.e. XANES and Raman)

pmst

🎨 Poor Man's Style Transfer - Painting an image with the style of another, without machine learning

pointillism

Create pointilized images using sprites or other sub images.

Comparing if the Emotion Detection from EEG data (derived from OpenBCI headset) and real-life Emotion Detection via webcam (OpenCV & Machine Learning) are matching i.e. if the emotions displayed on the face match the emotion perceived inside the brain.

polish_companies_bankruptcy

Mini machine learning project playing with feature and model selection

poppler

The poppler pdf rendering library

portfolioworkshop

AggieWorks x Aggie Sports Analytics Workshop Collaboration

pose2sim

Markerless kinematics with any cameras — From 2D Pose estimation to 3D OpenSim motion

posepile

Dataset processing scripts for large-scale 3D human pose estimation

poseviz

3D Human Pose Visualizer for Python

practical-machine-learning-with-python

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

practical_feature_engineering_guide

A detailed guide to feature engineering for machine learning in Python

predict-restaurant-rating

Predict the rating given to a restaurant based solely on the review text. Uses custom NLP primitives.

predicting-stock-movement-with-sentiment-analysis-on-sec-filings-using-nlp

By conducting sentiment analysis on a company's SEC filings and pairing with its stock price, the project explores machine learning models that best predicts the price of a stock.