Build flexible hierarchical multi-instance learning models.

Approximate Bayesian Computation (ABC) algorithms for likelihood free inference in julia

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

Evaluating Robustness of Neural Networks with Mixed Integer Programming

Bayesian optimization for Julia

Pure Julia implementations of single-pass object detection neural networks.

Sensible extensions for exposing torch in Julia.

OpenAI API wrapper for Julia

Beta Machine Learning Toolkit

Simple particle filter implementation in Julia - works with POMDPs.jl models or others.

Reservoir computing utilities for scientific machine learning (SciML)

Truncated singular value decomposition with partial reorthogonalization

Hyperparameter optimization algorithms for use in the MLJ machine learning framework

Utilities and abstractions for Machine Learning tasks

A Julia package for interpretable machine learning with stochastic Shapley values

Utility package for accessing common Machine Learning datasets in Julia

Create ensembles of machine learning models from scikit-learn, caret, and julia

A Julia package for data clustering

High-performance reactive message-passing based Bayesian inference engine

Home of the MLJ model registry and tools for model queries and mode code loading

Unofficial Julia bindings for logging experiments to wandb.ai

Julia implementation of the scikit-learn API https://cstjean.github.io/ScikitLearn.jl/dev/

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

Flux's ex AD

A Julia package for non-negative matrix factorization

A Julia wrapper for TensorFlow

Simple chains

A differentiable physics engine for robotics

Boosted trees in Julia

Julia implementation of Decision Tree (CART) and Random Forest algorithms

Julia package with selected functions in the R package `rethinking`. Used in the SR2... projects.

Flexible data augmentation library for machine and deep learning

Abstract types and methods for Gaussian Processes.

Implementation of advanced Sequential Monte Carlo and particle MCMC algorithms

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

Julia package of loss functions for machine learning.

Easy peasy logging to TensorBoard with Julia

A Julia package for training and evaluating multimodal deep Boltzmann machines

Generalized Linear Regressions Models (penalized regressions, robust regressions, ...)

Koç University deep learning framework.