Comments (5)
those are related.
But in particular I want API docs for gradient
, hessian
, jacobian
etc etc.
from abstractdifferentiation.jl.
Yes
Right now nothing explains what eg gradient
or hessian
is. What it's return types are etc
from abstractdifferentiation.jl.
There's the README, but do you mean an actual documentation website à la Documenter.jl?
from abstractdifferentiation.jl.
Related issues:
from abstractdifferentiation.jl.
This is solved in the main branch and corresponding dev docs
from abstractdifferentiation.jl.
Related Issues (20)
- Define a function for v' J v
- Expose tests through public API in src HOT 2
- Add docs for how to implement API as an AD HOT 2
- `pushforward_function` and `pullback_function` are confused by tuples vs single input
- Only support tuples and not single inputs/outputs HOT 7
- Use DocStringExtensions.jl
- Fix type instabilities HOT 7
- Add JET tests for linting and type instabilities
- AbstractDifferentiation stable docs 404 HOT 1
- Lazy jacobians could implement more of the LinearAlgebra interface
- New version timeline HOT 11
- Inconsistency in value_and_pushforward_function vs value_and_pullback_function outputs HOT 6
- Regression in type inferrability HOT 5
- Get rid of ugly macro? HOT 6
- `value_and_pullback_function` doesn't do what its docstring says HOT 3
- Configurable ForwardDiff tag
- Maybe AbstractDifferentiation should shrink to a collection of names? HOT 1
- Comparison with DifferentiationInterface.jl HOT 2
- Handling of thunks and tangents HOT 1
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from abstractdifferentiation.jl.