Paul Leiby's Projects
Course 18.S191 at MIT, fall 2020 - Introduction to computational thinking with Julia:
Advent of Code puzzles
https://www.coursera.org/learn/algorithms-on-graphs
Bayesian Statistics using Julia and Turing
Sample code for Channel 9 Python for Beginners course
Introduction to linear mixed models
coding experimentation
provides a modeling interface for mixed complementarity problems (MCP) and math programs with equilibrium problems (MPEC) via JuMP
notes and work files from Coursera deep learning courses
A book covering the fundamentals of data visualization.
Notebooks for learning deep learning
EIA Monthly Energy Review (MER) data: access and visualize/analyze
Applications of Data Science and Analysis to Energy
Evaluate Euro2020 and explore outcomes
Hands-On Design Patterns with Julia, published by Packt
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
The HilbertโHuang transform (HHT) is a way to decompose a signal into so-called intrinsic mode functions (IMF) along with a trend, and obtain instantaneous frequency data. It is designed to work well for data that is nonstationary and nonlinear.
This function contains a user friendly simulation of ventilation dynamics in honeybee nests. It is intended to accompany a manuscript submitted to Journal of the Royal Society Interface. The function allows for optional input so that the user can easily play with the parameters in the model and visualize the resulting dynamics.