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ai-102-aiengineer

Lab files for AI-102 - AI Engineer

alphaxiao1

Config files for my GitHub profile.

braingrid

A project to facilitate construction of high-performance neural simulations.

calciumbasedplasticitymodel

Python scripts implementing the calculations of synaptic changes as published in Graupner&Brunel (PNAS, 2012) as well as Graupner, Wallish & Ostojic (J Neurosci, 2016).

coding-the-matrix

The Coursera course Coding the Matrix: Linear Algebra through Computer Science Applications by Philip Klein

coding_the_matrix_solution

✒️ Solutions for all the chapters in 'Coding the Matrix: Linear Algebra through Computer Science Applications'

course-content

NMA Computational Neuroscience course

course_cmnp

This is the master code for creating the figures belonging to the course "Computational Modeling of Neuronal Plasticity" created by Florence Kleberg and Prof. Jochen Triesch, and released online for the INCF Learning Portal.

data-science-interviews

Data science interview questions and answers

deepxde

A library for scientific machine learning and physics-informed learning

dp-090-databricks-ml

firstcoursenetworkscience

Tutorials, datasets, and other material associated with textbook "A First Course in Network Science" by Menczer, Fortunato & Davis

handson-ml3

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.

linalg4datascience

Code that accompanies the book "Linear Algebra for Data Science"

mslearn-dp100

Lab files for Azure Machine Learning exercises

neuronaldynamics-exercises

Python exercises accompanying the book "Neuronal Dynamics"

notebooks

Notebooks for each chapter of several technical books, mostly in the domain of audio DSP. These are developed by me to go into further depth and enhance my understanding as I learn, and contain lots of animations, as well as working code examples and solutions (or attempts) at exercises. I hope they are helpful as an additional resource for other learners!

physics-informed-deep-learning-applications-to-experimental-fluid-mechanics

pinnpapers

Must-read Papers on Physics-Informed Neural Networks.

pinns

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

sitdl

Model of synaptic input time difference learning (SITDL), where each synapse can learn the time difference between glutamate and voltage signals.

spiking-neural-network

Pure python implementation of SNN

thinkstats2

Text and supporting code for Think Stats, 2nd Edition

thinkstats2_codebook

www-ged.msu.edu

Lab Web site at MSU