https://mrdimosthenis.github.io/Synapses/

A group of neural-network libraries for functional and mainstream languages
https://mrdimosthenis.github.io/Synapses/

clojure csharp deep-learning elixir fsharp functional-programming gleam java javascript lightweight machine-learning neural-network python scala

Last synced: about 2 months ago
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A group of neural-network libraries for functional and mainstream languages

• Host: GitHub
• URL: https://mrdimosthenis.github.io/Synapses/
• Owner: mrdimosthenis
• Created: 2019-11-24T17:51:43.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2021-09-23T20:55:47.000Z (about 3 years ago)
• Last Synced: 2024-05-21T01:01:07.463Z (7 months ago)
• Topics: clojure, csharp, deep-learning, elixir, fsharp, functional-programming, gleam, java, javascript, lightweight, machine-learning, neural-network, python, scala
• Homepage: https://mrdimosthenis.github.io/Synapses
• Size: 7.46 MB
• Stars: 68
• Watchers: 8
• Forks: 6
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-java - Synapses - Lightweight library for neural networks. (Projects / Machine Learning)

README

        
# Synapses

A group of **neural-network** libraries for functional and mainstream languages!

Choose a programming language:

* [Clojure](https://github.com/mrdimosthenis/clj-synapses)

* [C#](https://github.com/mrdimosthenis/SynapsesCSharp)

* [Elixir](https://github.com/mrdimosthenis/elixir_synapses)

* [F#](https://github.com/mrdimosthenis/FSharp.Synapses)

* [Gleam](https://github.com/mrdimosthenis/gleam_synapses)

* [Java](https://github.com/mrdimosthenis/synapses-java)

* [JavaScript](https://github.com/mrdimosthenis/synapses.js)

* [Python](https://github.com/mrdimosthenis/synapses-py)

* [Scala](https://github.com/mrdimosthenis/scala-synapses)

# Why Synapses?

## It's efficient

The implementation is based on *lazy list*.

The information flows smoothly.

Everything is obtained at a single pass.

## It's customizable

You can specify the **activation function** and the **weight distribution** for the neurons of each layer.

If this is not enough, edit the json of a network to be exactly what you have in mind.

## It offers visualizations

Get an overview of a neural network by taking a brief look at its **svg drawing**.

![Network Drawing](https://github.com/mrdimosthenis/Synapses/blob/master/network-drawing.png?raw=true)

## Data preprocessing is simple

By annotating the *discrete* and *continuous attributes*,

you can create a *preprocessor* that **encodes** and **decodes** the datapoints.

## Works for huge datasets

The functions that process big volumes of data, have an *Iterable/Stream* as argument.

RAM should not get full!

## It's well tested

Every function is tested for every language.

Take a look at the test projects.

## It's compatible across languages

The interface is similar across languages.

You can transfer a network from one platform to another via its **json instance**.

Create a neural network in *Python*, train it in *Java* and get its predictions in *JavaScript*!