The most well known is a 100% Julia neural network library called Flux.jl [1], which aims to become what Swift for Tensorflow wants as well (to make the entire Julia language a fully differentiable language) through Zygote.jl [2], and even without it has already great integration with the ecosystem, for example with the differentiable equations library through DiffEqFlux.jl [3]. Plus the source code is very high level (while being high performance, including easy GPU support), so you can easily see what each component does and implement any extension directly on your code without worrying about performance.
There is also another feature complete native library that allows some very concise code, Knet.jl [4], and the Tensorflow bindings [5].
The most well known is a 100% Julia neural network library called Flux.jl [1], which aims to become what Swift for Tensorflow wants as well (to make the entire Julia language a fully differentiable language) through Zygote.jl [2], and even without it has already great integration with the ecosystem, for example with the differentiable equations library through DiffEqFlux.jl [3]. Plus the source code is very high level (while being high performance, including easy GPU support), so you can easily see what each component does and implement any extension directly on your code without worrying about performance.
There is also another feature complete native library that allows some very concise code, Knet.jl [4], and the Tensorflow bindings [5].
[1] https://github.com/FluxML/Flux.jl
[2] https://github.com/FluxML/Zygote.jl
[3] https://julialang.org/blog/2019/01/fluxdiffeq
[4] https://github.com/denizyuret/Knet.jl
[5] https://github.com/malmaud/TensorFlow.jl