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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: 5 days 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 (almost 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 (6 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*!