Lux: Explicit Parameterization of Deep Neural Networks in Julia

Lux v0.5.54

Diff since v0.5.53

Merged pull requests:

• Test ComponentArrays with Enzyme (#653) (@avik-pal)
• Update DocumenterVitepress compat in docs (#654) (@asinghvi17)
• Minor optimizations (#681) (@avik-pal)
• CompatHelper: bump compat for Turing to 0.33 for package BayesianNN, (keep existing compat) (#688) (@github-actions[bot])
• Newer public functions (#690) (@avik-pal)
• Update Boltz API Docs (#691) (@avik-pal)
• Bump crate-ci/typos from 1.21.0 to 1.22.3 (#693) (@dependabot[bot])
• More API updates (#696) (@avik-pal)
• Add ReverseSequence (#698) (@NeroBlackstone)
• Training ConvMixer on CIFAR10 in 10mins (#700) (@avik-pal)
• Add activation functions doc reference (Rebase #694) (#702) (@avik-pal)
• Clean up the CI scripts (#703) (@avik-pal)
• Add test guide documentation (#705) (@NeroBlackstone)

Closed issues:

• Different activation functions in one layer (#680)
• Support for inactive arguments in DifferentiationInterface (#685)
• Predefined loss functions (#689)
• Static Type Parameters not accessible inside @compact (#692)