Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)

Machine learning and deep learning resources

Collection of Pytorch lightning tutorial form as rich scripts automatically transformed to ipython notebooks.

Welcome to the Physics-based Deep Learning Book (v0.2)

The fast.ai course notebooks

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.

Best Practices on Recommendation Systems

Online material for the Perspective: "Julia for Biologists" presented at JuliaCon 2021: https://www.youtube.com/watch?v=gRj7E5kYG1I

Course 18.S191 at MIT, Spring 2021 - Introduction to computational thinking with Julia:

🧑‍🏫 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit), optimizers (adam, radam, adabelief), gans(dcgan, cycle…

Lab Materials for MIT 6.S191: Introduction to Deep Learning

18.330 Introduction to Numerical Analysis

18.303 - Linear PDEs course

An ongoing repository of data on coronavirus cases and deaths in the U.S.

Text and supporting code for Modeling and Simulation in Python

Advanced Topics in Statistical Machine Learning, Oxford Statistics, Hilary Term 2020

A general purpose community detection and network embedding library for research built on NetworkX.

Course materials for Stat 133, Fall 2019, at UC Berkeley

The 3rd edition of course.fast.ai

IPython Cookbook, Second Edition, by Cyrille Rossant, Packt Publishing 2018

Resources for "Introduction to Deep Learning" course.

The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

Graph Convolutional Networks in PyTorch

Coding exercises for the Natural Language Processing concentration, part of Udacity's AIND program.

Repo for the Deep Learning Nanodegree Foundations program.

Project materials for RNN segment of AIND nanodegree