Grid-based approximation of partial differential equations in Julia

Repository of best practices for deep learning in Julia, inspired by fastai

Computer vision models for Flux

Robust robotic localization and mapping, together with NavAbility(TM). Reach out to info@wherewhen.ai for help.

Boosted trees in Julia

Wrapping deep learning models from the package Flux.jl for use in the MLJ.jl toolbox

Predictive Uncertainty Quantification through Conformal Prediction for Machine Learning models trained in MLJ.

Complex neural network examples for Flux.jl

Universal modeling and simulation of fluid mechanics upon machine learning. From the Boltzmann equation, heading towards multiscale and multiphysics flows.

A flexible neural net training library inspired by fast.ai

A package for time series data processing, classification, clustering, and prediction.

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Parallel distributed-memory version of Gridap

MagNav: airborne Magnetic anomaly Navigation

Julia Serialized Object (JLSO) file format for storing checkpoint data.

Imputation methods for missing data in julia

Implementation of the Deep Q-learning algorithm to solve MDPs

Clique recycling non-Gaussian (multi-modal) factor graph solver; also see Caesar.jl.

High-performance simulations of the Porous Electrode Theory for Li-ion batteries

Robot Motion Estimate: Tools, Variables, and Factors for SLAM in robotics; also see Caesar.jl.

A foolproof way of doing ML with GUI elements.

I heard you like compile times

Gmsh generated meshes for Gridap

Embedded finite element methods in Julia

Effortless Bayesian Deep Learning through Laplace Approximation for Flux.jl neural networks.

Makie plotting recipes for Gridap

Time stepping for Gridap

Neural Network based models for Natural Language Processing

Provides PETSc solvers to the Gridap project

What were my hyper-parameter settings again?

OpenMendel package for haplotyping and imputation

Neural network training, fast and easy.

Random Integrators for many-body quantum systems

Flux reconstruction method for advection-diffusion type physics

A Meta Package for Machine Learning Interpretation

Reproducible tests for HTTP requests

Package for Bayesian and classical estimation and inference based on statistics that are filtered through a trained neural net

Take your packages for a jog!

Method of Characteristics neutral particle transport code for reactor physics written in Julia.

SeaPearl's pool of examples