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


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

Choose a programming language:

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

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!