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emel
emel provides functional machine learning tools for developers using the Erlang ecosystem.

About emel
emel is a functional machine learning library (re)implemented in Gleam for the Erlang ecosystem. It turns training data into functions that classify or predict new inputs, with implementations for linear regression, k-nearest neighbors, decision tree, naive Bayes, k-means, perceptron, logistic regression, and neural network, plus algebra, geometry, and statistics helpers.
It can be added to Erlang projects through rebar.config, Elixir projects through mix.exs, and Gleam projects with gleam add, with usage examples shown for each language.
Key features
- K-nearest neighbors classifier that returns a function for new data
- Linear regression predictor that returns a function for new data
- Neural network classifier with settable hidden layers and training parameters
- Decision tree, naive Bayes, perceptron and logistic regression models
- K-means clustering implementation
- Math helpers for algebra, geometry and statistics
- Supports Erlang, Elixir, and Gleam projects with specific setup instructions
- Includes usage examples in Erlang, Elixir, and Gleam
- Implements eight learning algorithms plus algebra, geometry, and statistics helpers
emel at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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emel FAQ
How do I add emel to an Erlang project?
How do I add emel to an Elixir project?
How do I add emel to a Gleam project?
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