holdenb
Sr. Software Engineer | Robotics & Control for Space Systems | Current MS-ECE Student @ Purdue University
Purdue UniversityLafayette, Louisiana
Pinned Repositories
city-of-poets
Holden Babineaux's Personal Site & Blog
HoldenB
"About Me" page which includes my personal bio, experience, and education.
lpcv-2023-tiny-espresso-net
A tiny CNN for on-device disaster scene parsing and semantic segmentation for the lpcv.ai 2023 challenge.
mojo-num-analysis
Numerical analysis and algorithms for numerical linear algebra written in Mojo. This project is inspired by Justin Solomon's lectures on Mathematical Methods for Robotics, Vision, and Graphics.
professioneer
A World of Warcraft (classic) profession estimation system that simulates crafting and uses Monte Carlo estimation to predict viable crafting routes.
pycvss
A python module for the ULL High Performance Cloud Computing (HPCC) lab's Cloud Video Streaming Service (CVSS) architecture designed for research and service provision.
quantified-self-sensor-processing
Postprocessing and analysis of IMU data for health-related metrics and classification tasks. Reference: Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data - Mark Hoogendoorn & Burkhardt Funk
TD3-BC-from-scratch
Re-implementation of the original TD3+BC algorithm for offline reinforcement learning by Fujimoto & Gu, 2021.
holdenb's Repositories
holdenb/lpcv-2023-tiny-espresso-net
A tiny CNN for on-device disaster scene parsing and semantic segmentation for the lpcv.ai 2023 challenge.
holdenb/TD3-BC-from-scratch
Re-implementation of the original TD3+BC algorithm for offline reinforcement learning by Fujimoto & Gu, 2021.
holdenb/city-of-poets
Holden Babineaux's Personal Site & Blog
holdenb/engi_analysis_notebooks
holdenb/HoldenB
"About Me" page which includes my personal bio, experience, and education.
holdenb/lpcv-tiny-espresso-net
holdenb/n_net_from_scratch
holdenb/quantified-self-sensor-processing
Postprocessing and analysis of IMU data for health-related metrics and classification tasks. Reference: Machine Learning for the Quantified Self: On the Art of Learning from Sensory Data - Mark Hoogendoorn & Burkhardt Funk