IvanYashchuk

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Automatic Differentiation Handbook

AdvancedHMC.jl

Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms

chainer

A flexible framework of neural networks for deep learning

cmdstan-petsc

CUDA.jl

CUDA programming in Julia.

FEMMaterials.jl

JuliaFEM bindings to Materials.jl

fenics_pymccon2020

PyMCon 2020 Tutorial on using FEniCS with PyMC3

Flux.jl

Relax! Flux is the ML library that doesn't make you tensor

gsoc

Numfocus Google Summer of Code Materials

jax

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

MFrontGenericInterfaceSupport

This project aims at providing support for MFront generic behaviours. This project can be embedded in open-source and propriary sofware

nb_repo

numerical-mooc

A course in numerical methods with Python for engineers and scientists: currently 5 learning modules, with student assignments.

numpy.org

The NumPy home page

pencil-code

A high-order finite-difference code for compressible hydrodynamic flows with magnetic fields and particles

pymc3

Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

stan-math-petsc

The Stan Math Library is a C++ template library for automatic differentiation of any order using forward, reverse, and mixed modes. It includes a range of built-in functions for probabilistic modeling, linear algebra, and equation solving.