A lightweight library for neural networks that runs anywhere
A group of neural-network libraries for functional and mainstream languages!
Choose a programming language:
The implementation is based on lazy list.
The information flows smoothly.
Everything is obtained at a single pass.
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.
Get an overview of a neural network by taking a brief look at its svg drawing.
By annotating the discrete and continuous attributes,
you can create a preprocessor that encodes and decodes the datapoints.
The functions that process big volumes of data, have an Iterable/Stream as argument.
RAM should not get full!
Every function is tested for every language.
Take a look at the test projects.
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!