Showing 4 open source projects for "automatic"

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  • 1
    DiffEqFlux.jl

    DiffEqFlux.jl

    Pre-built implicit layer architectures with O(1) backprop, GPUs

    DiffEqFlux.jl is a Julia library that combines differential equations with neural networks, enabling the creation of neural differential equations (neural ODEs), universal differential equations, and physics-informed learning models. It serves as a bridge between the DifferentialEquations.jl and Flux.jl libraries, allowing for end-to-end differentiable simulations and model training in scientific machine learning. DiffEqFlux.jl is widely used for modeling dynamical systems with learnable...
    Downloads: 1 This Week
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  • 2
    NeuralPDE.jl

    NeuralPDE.jl

    Physics-Informed Neural Networks (PINN) Solvers

    NeuralPDE.jl is a Julia library for solving partial differential equations (PDEs) using physics-informed neural networks and scientific machine learning. Built on top of the SciML ecosystem, it provides a flexible and composable interface for defining PDEs and training neural networks to approximate their solutions. NeuralPDE.jl enables hybrid modeling, data-driven discovery, and fast PDE solvers in high dimensions, making it suitable for scientific research and engineering applications.
    Downloads: 0 This Week
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  • 3
    Zygote

    Zygote

    21st century AD

    Zygote provides source-to-source automatic differentiation (AD) in Julia, and is the next-gen AD system for the Flux differentiable programming framework. For more details and benchmarks of Zygote's technique, see our paper. You may want to check out Flux for more interesting examples of Zygote usage; the documentation here focuses on internals and advanced AD usage.
    Downloads: 0 This Week
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  • 4
    Knet

    Knet

    Koç University deep learning framework

    Knet.jl is a deep learning package implemented in Julia, so you should be able to run it on any machine that can run Julia. It has been extensively tested on Linux machines with NVIDIA GPUs and CUDA libraries, and it has been reported to work on OSX and Windows. If you would like to try it on your own computer, please follow the instructions on Installation. If you would like to try working with a GPU and do not have access to one, take a look at Using Amazon AWS or Using Microsoft Azure. If...
    Downloads: 0 This Week
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