Flux.jl
Elegant, 100% pure-Julia deep learning stack with first-class GPU support, differentiable programming, and a broad model-zoo ecosystem.
Visit siteKnet.jl
Koç University's deep learning framework, supporting GPU operation and automatic differentiation using dynamic computational graphs defined in plain Julia.
Visit siteTensorFlow.jl
A Julia wrapper around Google's TensorFlow, bringing state-of-the-art deep learning models to Julia with an API closely mirroring the Python original.
Visit siteScikitLearn.jl
Brings scikit-learn's uniform model interface, pipelines, evaluation, and hyperparameter tuning tools to Julia, integrating Julia- and Python-defined models.
Visit siteApache MXNet
Lightweight, portable, flexible deep learning framework with dynamic dataflow scheduling, supporting Python, R, Julia, Scala, Go, and JavaScript.
Note: the Apache MXNet project has since retired; docs remain live for reference. Visit siteMLBase.jl
A "Swiss knife" toolkit of functions supporting machine learning development: data preprocessing, classification scoring, performance evaluation, and cross-validation.
Visit siteMerlin.jl
A deep learning framework written in Julia, aiming to provide a fast, flexible, and compact library for building machine learning models.
Visit siteStrada.jl
A deep learning library for Julia based on the popular Caffe framework, supporting convolutional and recurrent neural network training on CPU and GPU.
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