Скачать или смотреть A Tour of the differentiable programming landscape with Flux.jl | Dhairya Gandhi | JuliaCon 2021

This talk was presented as part of JuliaCon 2021.

Abstract:
Deep learning has grown steadily and there has been rising interest from various groups to incorporate ML techniques in their modelling via differentiable programming. Software 2.0 as its known, is going to need a large resource pool of tools to actualise its goal. In this talk, we will discuss how the Flux.jl stack along with Zygote and next-gen AD tooling is enabling differentiable programming already in a variety of domains and tour across the packages and projects that are taking part in it.

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Contents
0:00 Welcome!
0:34 Technical note
0:47 Chemellia.jl
1:27 DifferentiableDFTK: automatic differentiation for density functional theory in Julia
2:26 Molly.jl and Zygote.jl
3:06 Flux3D.jl a 3D vision library
3:59 SciML must be mentioned
4:12 ∂P as a tool
4:50 Dagger.jl and Flux
6:58 Large scale distributing training in Julia
7:33 Optimisers.jl

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