Optimisers.jl defines many standard optimisers and utilities for learning loops.

Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable i…

A comprehensive collection of KAN(Kolmogorov-Arnold Network)-related resources, including libraries, projects, tutorials, papers, and more, for researchers and developers in the Kolmogorov-Arnold N…

Learn fast, scalable, and calibrated measures of uncertainty using neural networks!

CUDA programming in Julia.

Reusable array functionality for Julia's various GPU backends.

Arrays with arbitrarily nested named components.

Distributed Data Parallel Training of Deep Neural Networks

Common hyperparameter scheduling for ML

Relax! Flux is the ML library that doesn't make you tensor

A Julia framework for implementing FFTs

Forward Mode Automatic Differentiation for Julia

High-performance and differentiation-enabled nonlinear solvers

21st century AD

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Julia bindings to the FFTW library for fast Fourier transforms

Explicitly Parameterized Neural Networks in Julia

A 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…

The Base interface of the SciML ecosystem

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

High performance differential equation solvers for ordinary differential equations, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)

Fast jacobian computation through sparsity exploitation and matrix coloring