Flexible data augmentation library for machine and deep learning

Evolve Flux networks from scratch!

A Julia package for training and evaluating multimodal deep Boltzmann machines

Truncated singular value decomposition with partial reorthogonalization

Machine Learning in Julia

TopicModels for Julia

Sum-product networks in Julia.

PEG Parser for Julia

Lightweight package to interface with MLJ

Workshop materials for training in scientific computing and scientific machine learning

A Julia package for Princeton's WordNet®.

Generic artificial neural networks in Julia

Machine learning for sequential/streaming data

Automatic probabilistic programming for scientific machine learning and dynamical models

A fast TOML parser for TOML 1.0 written in Julia

Mel Frequency Cepstral Coefficients calculation for Julia

A deep learning library for Julia based on Caffe

A variety of loaders for various NLP corpora.

Utility package for working with classification targets and label-encodings

BayesianNonparametrics in julia

A lightweight julia wrapper for WORLD - a high-quality speech analysis, modification and synthesis system

Utilities to efficiently track learning curves or other optimization information

A simple Julia implementation of finite state machines.

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

Extreme Learning Machine in julia

KDTrees for julia

Gradient boosting framework for Julia.

Example of distributed learning in Julia

Julia binding of Japanese morphological analyzer MeCab

A spiking neural network simulator written in Julia.

Mel-Generalized Cepstrum analysis

Run keras models with a Flux backend

A Julia package for data discretization and label maps

Hard-Forked from JuliaText/TextAnalysis.jl

Uniform interface for machine learning in Julia

Abstractions for Julia Machine Learning Packages

A reliable leaderboard algorithm for machine learning competitions

Utilities for English-language quirks in Julia

Julia code for the paper S. Gu, L. Zhang, W. Zuo, and X. Feng, “Projective Dictionary Pair Learning for Pattern Classification,” In NIPS 2014

Julia code for the paper S. Gu, L. Zhang, W. Zuo, and X. Feng, “Projective Dictionary Pair Learning for Pattern Classification,” In NIPS 2014