Dependency Packages
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Flux.jl4122Relax! Flux is the ML library that doesn't make you tensor
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Turing.jl1807Bayesian inference with probabilistic programming.
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Zygote.jl135321st century AD
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AlphaZero.jl1131A generic, simple and fast implementation of Deepmind's AlphaZero algorithm.
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DiffEqFlux.jl771Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
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NeuralPDE.jl755Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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NeuralNetDiffEq.jl755Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
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DiffEqTutorials.jl694Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
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FastAI.jl557Repository of best practices for deep learning in Julia, inspired by fastai
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Transformers.jl420Julia Implementation of Transformer models
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SymbolicRegression.jl377Distributed High-Performance symbolic regression in Julia
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GeometricFlux.jl330Geometric Deep Learning for Flux
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Metalhead.jl297Computer vision models for Flux
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Molly.jl281Molecular simulation in Julia
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Surrogates.jl281Surrogate modeling and optimization for scientific machine learning (SciML)
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DiffEqSensitivity.jl248A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
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SciMLSensitivity.jl248A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
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BAT.jl164A Bayesian Analysis Toolkit in Julia
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Omega.jl155Causal, Higher-Order, Probabilistic Programming
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GraphNeuralNetworks.jl153Graph Neural Networks in Julia
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TuringModels.jl153Implementations of the models from the Statistical Rethinking book with Turing.jl
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NeuralOperators.jl151DeepONets, (Fourier) Neural Operators, Physics-Informed Neural Operators, and more in Julia
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Yota.jl145Reverse-mode automatic differentiation in Julia
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DistributionsAD.jl142Automatic differentiation of Distributions using Tracker, Zygote, ForwardDiff and ReverseDiff
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StochasticAD.jl142Research package for automatic differentiation of programs containing discrete randomness.
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RayTracer.jl141Differentiable RayTracing in Julia
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TopOpt.jl141A beautifully Julian topology optimization package.
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SeaPearl.jl141Julia hybrid constraint programming solver enhanced by a reinforcement learning driven search.
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AugmentedGaussianProcesses.jl132Gaussian Process package based on data augmentation, sparsity and natural gradients
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DiffEqBayes.jl117Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning
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MLJFlux.jl115Wrapping deep learning models from the package Flux.jl for use in the MLJ.jl toolbox
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FluxArchitectures.jl113Complex neural network examples for Flux.jl
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ProximalAlgorithms.jl106Proximal algorithms for nonsmooth optimization in Julia
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Avalon.jl105Starter kit for legendary models
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InvertibleNetworks.jl101A Julia framework for invertible neural networks
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Kinetic.jl96Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.
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FluxTraining.jl95A flexible neural net training library inspired by fast.ai
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Flux3D.jl913D computer vision library in Julia
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TemporalGPs.jl91Fast inference for Gaussian processes in problems involving time. Partly built on results from https://proceedings.mlr.press/v161/tebbutt21a.html
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Nonconvex.jl91Toolbox for non-convex constrained optimization.
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