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.
Data preprocessing is simpleBy 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.
It's compatible across languagesThe 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!
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