# Deep learning in Julia

**URL:** <https://discourse.julialang.org/t/deep-learning-in-julia/112844>\
**Category:** Machine Learning\
**Created:** [April 11, 2024, 7:53pm UTC](https://discourse.julialang.org/t/deep-learning-in-julia/112844 "2024-04-11T19:53:30Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![cpfiffer](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/cpfiffer/32/208747_2.png) [@cpfiffer](https://discourse.julialang.org/u/cpfiffer)\
**Post date:** [April 11, 2024, 8:05pm UTC](https://discourse.julialang.org/t/deep-learning-in-julia/112844/2 "2024-04-11T20:05:57Z")

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Also: this discussion of Lux.jl is great and topical.

> [@\[ANN\] Lux.jl: Explicitly Parameterized Neural Networks in Julia](https://discourse.julialang.org/t/ann-lux-jl-explicitly-parameterized-neural-networks-in-julia/81689):
>
> [Lux](https://github.com/avik-pal/Lux.jl) is a new Julia deep learning framework that decouples models and parameterization using deeply nested named tuples. Functional Layer API – Pure Functions and Deterministic Function Calls. No more implicit parameterization Compiler and AD-friendly Neural Networks using Lux, Random, Optimisers, Zygote # Seeding rng = Random.default\_rng() Random.seed!(rng, 0) # Construct the layer model = Chain( BatchNorm(128), Dense(128, 256, tanh), BatchNorm(256), Chain( Dense(256…

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