On: Jax – Composable transformations of Python and NumPy programs

[oxinabox]

We have so many AD packages than that in julia. Julia suffers the lisp problem for AD. http://winestockwebdesign.com/Essays/Lisp_Curse.html It is a real problem.

One of my on going projects is ChainRules (http://www.juliadiff.org/ChainRulesCore.jl/dev/) which will unite them under one set of custom senstitivities and more generally make it easier to mix and match them

I need to update the JuliaDiff website, I want to list all of them in a table with some some key points.

Forward mode:

- [ForwardDiff](https://github.com/JuliaDiff/ForwardDiff.jl)

- [ForwardDiff2](https://github.com/YingboMa//ForwardDiff2.jl)

Reverse Mode:

- [Nabla](https://github.com/invenia/Nabla.jl/)

- [Tracker](https://github.com/FluxML/Tracker.jl)

- [Yota](https://github.com/dfdx/Yota.jl)

- [Zygote](https://github.com/FluxML/Zygote.jl)

- [ReverseDiff](https://github.com/JuliaDiff/ReverseDiff.jl)

- [AutoGrad.jl](https://github.com/denizyuret/AutoGrad.jl)

- [NiLang](https://github.com/GiggleLiu/NiLang.jl) (arguably not reverse mode)

Symbolic:

- [ModelingToolKit](https://github.com/JuliaDiffEq/ModelingToolkit.jl)

- [XGrad.jl](https://github.com/dfdx/XGrad.jl)

Finite Differencing:

- [Calculus](https://github.com/JuliaMath/Calculus.jl) (please stop)

- [FiniteDifferences](https://github.com/JuliaDiff/FiniteDifferences.jl)

- [FiniteDiff](https://github.com/JuliaDiff/FiniteDiff.jl)