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AbstractDifferentiation.jl for AD-backend agnostic code

frankschae.github.io · published 1 August 2021

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https://sinews.siam.org/Details-Page/scientific-machine-learning-how-julia-employs-differentiable-programming-to-do-it-best
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https://github.com/tensorflow/swift/blob/main/docs/WhySwiftForTensorFlow.md
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https://www.youtube.com/watch?v=uDUwdAqKzYM&list=PLP8iPy9hna6TxktMt-IzdU2vQpGp3bwDn&index=3&t=12s
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https://juliadiff.org/
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https://discourse.julialang.org/t/state-of-automatic-differentiation-in-julia/43083
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https://github.com/JuliaDiff/ChainRules.jl
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Glue AD for Full Language Differentiable Programming Read at the source ↗
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https://github.com/mohamed82008
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https://github.com/JuliaDiff/AbstractDifferentiation.jl/pull/1
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https://github.com/JuliaDiff/AbstractDifferentiation.jl
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https://github.com/SciML/DiffEqSensitivity.jl
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https://en.wikipedia.org/wiki/Gauss%E2%80%93Newton_algorithm
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https://github.com/JuliaDiff/FiniteDifferences.jl
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https://github.com/FluxML/Zygote.jl
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https://discourse.julialang.org/t/help-with-jacobian-vector-product-to-get-natural-gradient/51115/12
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https://github.com/wsmoses/Enzyme.jl
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https://github.com/SciML/DiffEqSensitivity.jl/pull/427
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https://diffeq.sciml.ai/stable/analysis/sensitivity/
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https://en.wikipedia.org/wiki/Newton%27s_method
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https://en.wikipedia.org/wiki/Newton%27s_method_in_optimization
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https://github.com/JuliaDiff/Diffractor.jl
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