Differentiate python calls from Julia
MIT License
An interface to various automatic differentiation backends in Julia.
Fast operator-overloading Jacobian & Hessian sparsity detection.
CPU/GPU parallel performance portable layer in Julia via functions as arguments
The Julia C++ Interface
A framework for composing Neural Processes in Julia
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Solving differential equations in parallel on GPUs - JuliaCon 2021 workshop
TensorOperations.jl compatible fast contractor for Julia, based on TBLIS, with generic strides an...
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Same-same but different
Python and Julia in harmony.
It slices, it dices, it splices!
Types with default field values, keyword constructors and (un-)pack macros
A tool for converting specific Julia GPU code writen in CUDA.jl, into abstract multi-backend code...
Cross-architecture parallel algorithms for Julia's GPU backends, from a unified KernelAbstraction...