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02456-deep-learning

Exercises and supplementary material for the deep learning course 02456.

02456-deep-learning-with-pytorch

Exercises and supplementary material for the deep learning course 02456 using PyTorch.

02457

Python port of the exercises from DTU course "Nonlinear Signal Processing"

100-days-of-ml-code

100 Days of ML Coding

100-days-of-ml-code-1

100-Days-Of-ML-Code中文版

18330

18.330 Intro to Numerical Analysis

18335

18.335 - Introduction to Numerical Methods course

18s096-iap17

18.S096 course at MIT in IAP 2017

18s096sciml

18.S096 - Applications of Scientific Machine Learning

2010-laa-numerical-cs

C. Dossal, G. Peyré, J. Fadili. A Numerical Exploration of Compressed Sampling Recovery. Linear Algebra and Applications, 432(7), pp. 1663–1679, 2010.

2016-datascience-labs

Labs for Introduction to Data Science @ U of U

2016_best_projects

The best projects of the Introduction to Data Science class 2016

2017-ot-beginners

Optimal Transport for Dummies - Code, slides and article

2018-cs109a

Repository for CS109A Fall 2018

2018-idl

Introduction to Deep Learning

2018-idl-projects

Student projects of the Introduction to Deep Learning winter school 2018.

2018fall_228a

Numerical Solution of Differential Equations

2019

Materials for Applied Data Analysis CS-401, fall 2019)

2022-sustech-julia-workshop

Material for the SUStech Julia workshop from 5th till 7th October 2022

50-git-questions

50 Frequently Asked question about Git & GitHub

6s083

Materials for MIT 6.S083 / 18.S190: Computational thinking with Julia + application to the COVID-19 pandemic

a-bit-of-deep-learning-and-keras

Follow along repo for the eponymously named youtube playlist

academic-kickstart

access-julia

Introductory tutorial in Julia for the ACCESS meeting.

active_gp_hyperlearning

Active learning of GP hyperparameters following Garnett, et al., "Active Learning of Linear Embeddings for Gaussian Processes," (UAI 2014).

adaptive-lookup-table-search-on-gpu

adaptivegp

Fully nonstationary, heteroscedastic GP for Matlab

adsi_computational_imaging

Resources for the lab on computational imaging at the ADSI Summer School 2019 in Seattle, WA

advanced-deep-learning-with-keras

Advanced Deep Learning with Keras, published by Packt

advanced-machine-learning

Machine Learning: From Theory to Practise