SynapsesCSharp 7.3.1

A lightweight library for neural networks that runs anywhere

Install-Package SynapsesCSharp -Version 7.3.1
dotnet add package SynapsesCSharp --version 7.3.1
<PackageReference Include="SynapsesCSharp" Version="7.3.1" />
For projects that support PackageReference, copy this XML node into the project file to reference the package.
paket add SynapsesCSharp --version 7.3.1
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Synapses

A lightweight library for neural networks that runs anywhere!

Network Video

Getting Started

Why Sypapses?

It's easy
  1. Add one dependency to your project.
  2. Write a single import statement.
  3. Use a few pure functions.

You are all set!

It runs anywhere <sup>1</sup>

Supported languages:

It's compatible across languages
  1. The interface is common across languages.
  2. 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!
It offers visualizations

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

Network Drawing

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 instance of a network to be exactly what you have in mind.

It's efficient

The implementation is based on lazy list.
The information flows smoothly.
Everything is obtained at a single pass.

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.
Please take a look at the test projects.

Dependencies

Misc

JetBrains

JetBrains tools have helped for this project!

Synapses

A lightweight library for neural networks that runs anywhere!

Network Video

Getting Started

Why Sypapses?

It's easy
  1. Add one dependency to your project.
  2. Write a single import statement.
  3. Use a few pure functions.

You are all set!

It runs anywhere <sup>1</sup>

Supported languages:

It's compatible across languages
  1. The interface is common across languages.
  2. 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!
It offers visualizations

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

Network Drawing

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 instance of a network to be exactly what you have in mind.

It's efficient

The implementation is based on lazy list.
The information flows smoothly.
Everything is obtained at a single pass.

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.
Please take a look at the test projects.

Dependencies

Misc

JetBrains

JetBrains tools have helped for this project!

NuGet packages

This package is not used by any NuGet packages.

GitHub repositories

This package is not used by any popular GitHub repositories.

Version History

Version Downloads Last updated
7.3.1 160 4/12/2020
7.3.0 163 3/8/2020
7.2.1 139 2/2/2020
7.1.2 154 1/19/2020