Abstractions, algorithms, and utilities for reinforcement learning in Julia

Utility package for accessing common Machine Learning datasets in Julia

Julia package of loss functions for machine learning.

OpenAI's Gym binding for Julia

Utility package for generating, loading, splitting, and processing Machine Learning datasets

LIBSVM bindings for Julia

Utility package for subsetting, resampling, iteration, and partitioning of various types of data sets in Machine Learning

Arcade Learning Environment (ALE) wrapped as a Reinforce.jl environment

Static transforms, activation functions, and other implementations of LearnBase abstractions

Utility package for working with classification targets and label-encodings

A generic and modular framework for building custom iterative algorithms in Julia

Implementations of stochastic optimization algorithms and solvers

Metrics for scoring machine learning models in Julia

Plotting recipes for statistics and machine learning using Plots.jl

Julia package of regularization functions for machine learning

Utilities to efficiently track learning curves or other optimization information

Abstractions for Julia Machine Learning Packages

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A playground for implementations of unconstrained continuous full-batch optimization algorithms

A Julia package providing access to the OpenAI Gym API