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18s096-iap17

18.S096 course at MIT in IAP 2017

abstractmcmc.jl

Abstract types and interfaces for Markov chain Monte Carlo methods

adnlpmodels.jl

algebraicmultigrid.jl

Algebraic Multigrid in Julia

almcourse

Materials for the TOPology OPTimization course [ALM]

amsi-winter-school

Materials for the AMSI Winter School

amsiwinterschool2021

amsiws2021_neural_network_workshop

arrayiteration.jl

Testing new ideas for array iteration

atomics

bayesian-neural-networks

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

bijectors.jl

blosc.jl

Blosc compression for the Julia language

branchandbound.jl

This package implements an abstract branch and bound framework and algorithms with emphasis on JuMP-based optimization and MIP.

branchandbound.jl-1

A Julia framework for implementing branch-and-bound-type algorithms

calipso.jl

Conic Augmented Lagrangian Interior-Point SOlver

chainrules.jl

forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs

checkpointing.jl

Checkpointing for Automatic Differentiation

cmdstan.jl

Stan from Julia (Julia v1.0, v1.1 and v1.2)

computexp.jl

An experimental package to manage computational experiments.

convergenceplots.jl

Visualizing convergence was never easier.

cuarrays.jl

A Curious Cumulation of CUDA Cuisine

cudasupport.jl

Umbrella package for CUDA-related packages for use with Requires.jl.

dalle-mtf

Open-AI's DALL-E for large scale training in mesh-tensorflow.

datadrivendiffeq.jl

Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization

diffeqbase.jl

The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems

diffeqbayes.jl

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations

diffeqsensitivity.jl

A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc.

differentiablefactorizations.jl

Differentiable matrix factorizations using ImplicitDifferentiation.jl.

difftests.jl

A common suite of test functions for stressing the robustness of differentiation tools.