Name: David R. Pugh
Type: User
Company: King Abdullah University of Science and Technology
Bio: Instructional Assistant Professor in AI, KAUST; Certified Instructor, @swcarpentry;
Twitter: TheSandyCoder
Location: Thuwal, Saudi Arabia
David R. Pugh's Projects
Workshop website for APERC Software Carpentry Workshop
Website for Spring 2021 Introduction to Python for Data Science Workshop
Collection of Jupyter notebooks on algorithms and data structures
Code for solving Eeckhout and Kircher model of assortative matching between heterogenous firms and workers.
Repository for code from my blog...
Recipes of the IPython Cookbook, the definitive guide to high-performance scientific computing and data science in Python
Example project template from my DockerCon 2021 talk
Graduate level econometrics labs in Python/R
Model of the co-evolution by natural selection of an individually costly cooperative behavior trait together with a preference for social partners who bear that trait.
Working notes on functional programming (in Scala) for economists...
Work completed for the Functional Programming in Scala specialization on Coursera
Template repository for a Python 3-based data science project that uses Horovod.
Code for "Image Distribution" Chapter of Using Docker
Repository of course materials for a multi-day course on accelerated machine learning.
Materials for a multi-day course on computer vision
Course materials for a multi-day course on deep learning.
Course materials for a multi-day course on graph neural networks
Template repository for a Jax-based (data) science project with GPU acceleration
Forecasting Solar Power for NEOM
Repository of materials for my JupyterCon 2020 talk
Repository of course materials for a multi-day course on machine learning for tabular data using Scikit-Learn and XGBoost
Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)
Graduate level course on numerical methods for economists
Template repository for a Python 3-based (data) science project with GPU acceleration using NVIDIA RAPIDS libraries.