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https://github.com/oml-npa/easyml.jl

A foolproof way of doing ML with GUI elements.
https://github.com/oml-npa/easyml.jl

flux gui julia-language machine-learning

Last synced: 4 days ago
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A foolproof way of doing ML with GUI elements.

Host: GitHub
URL: https://github.com/oml-npa/easyml.jl
Owner: OML-NPA
License: other
Created: 2021-01-06T20:20:25.000Z (over 3 years ago)
Default Branch: main
Last Pushed: 2022-01-11T13:12:04.000Z (over 2 years ago)
Last Synced: 2024-09-25T22:09:48.271Z (8 days ago)
Topics: flux, gui, julia-language, machine-learning
Language: QML
Homepage:
Size: 240 MB
Stars: 51
Watchers: 1
Forks: 3
Open Issues: 1
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE

Awesome Lists containing this project

README

        


  



EasyML.jl


[![docs stable](https://img.shields.io/badge/docs-stable-blue.svg)](https://oml-npa.github.io/EasyML.jl/stable/)

[![docs dev](https://img.shields.io/badge/docs-dev-blue.svg)](https://oml-npa.github.io/EasyML.jl/dev/)

[![CI main](https://github.com/OML-NPA/EasyML.jl/actions/workflows/CI-main.yml/badge.svg)](https://github.com/OML-NPA/EasyM.jl/actions/workflows/CI-main.yml)

[![CI dev](https://github.com/OML-NPA/EasyML.jl/actions/workflows/CI-dev.yml/badge.svg)](https://github.com/OML-NPA/EasyM.jl/actions/workflows/CI-dev.yml)

[![codecov](https://codecov.io/gh/OML-NPA/EasyML.jl/branch/main/graph/badge.svg?token=BTAK4ZYPGG)](https://codecov.io/gh/OML-NPA/EasyML.jl)

This package allows to use machine learning in Julia through a graphical user interface.

NB! This is a beta version. Bugs and breaking changes should be expected.

The package will be updated to Julia 1.7 as soon as [QML.jl](https://github.com/barche/QML.jl) is updated.

If you are interested in the package and would like to contribute then write here: [email protected].

## Features

It is possible to:

  - Design a neural network

  - Train a neural network

  - Validate a neural network

  - Apply a neural network to new data

  

Classification, regression and segmentation on images are currently supported.

[Flux.jl](https://github.com/FluxML/Flux.jl) machine learning library is used under the hood.

  

## Installation

Run `] add EasyML` in REPL.

If fonts do not look correct then install [this](https://github.com/OML-NPA/EasyML.jl/raw/main/src/fonts/font.otf) and [this](https://github.com/OML-NPA/EasyML.jl/raw/main/src/fonts/font_bold.otf) font.

## Quick guide

EasyML is easy enough to figure out by yourself! Just run the following lines.

### Add the package

```julia

using EasyML

```

### Set up

```julia

change(global_options)

```

### Design

```julia

change_classes()

design_model()

```

### Train

```julia

change(data_preparation_options)

change(training_options)

get_urls_training()

get_urls_testing()

prepare_training_data()

prepare_testing_data()

results = train()

remove_training_data()

remove_testing_data()

remove_training_results()

```

### Validate

```julia

change(validation_options)

get_urls_validation()

results = validate()

remove_validation_data()

remove_validation_results()

```

### Apply

```julia

change(application_options)

change_output_options()

get_urls_application()

apply()

remove_application_data()

```

### On reopening

```julia

load_model()

load_options()

```

## Development

A plan for the project can be seen [here](https://github.com/OML-NPA/EasyML.jl/projects/2).