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