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Synapses\n\nA group of **neural-network** libraries for functional and mainstream languages!\n\nChoose a programming language:\n\n* [Clojure](https://github.com/mrdimosthenis/clj-synapses)\n* [C#](https://github.com/mrdimosthenis/SynapsesCSharp)\n* [Elixir](https://github.com/mrdimosthenis/elixir_synapses)\n* [F#](https://github.com/mrdimosthenis/FSharp.Synapses)\n* [Gleam](https://github.com/mrdimosthenis/gleam_synapses)\n* [Java](https://github.com/mrdimosthenis/synapses-java)\n* [JavaScript](https://github.com/mrdimosthenis/synapses.js)\n* [Python](https://github.com/mrdimosthenis/synapses-py)\n* [Scala](https://github.com/mrdimosthenis/scala-synapses)\n\n# Why Synapses?\n\n## It's efficient\n\nThe implementation is based on *lazy list*.\nThe information flows smoothly.\nEverything is obtained at a single pass.\n\n## It's customizable\n\nYou can specify the **activation function** and the **weight distribution** for the neurons of each layer.\nIf this is not enough, edit the json of a network to be exactly what you have in mind.\n\n## It offers visualizations\n\nGet an overview of a neural network by taking a brief look at its **svg drawing**.\n\n![Network Drawing](https://github.com/mrdimosthenis/Synapses/blob/master/network-drawing.png?raw=true)\n\n## Data preprocessing is simple\n\nBy annotating the *discrete* and *continuous attributes*,\nyou can create a *preprocessor* that **encodes** and **decodes** the datapoints.\n\n## Works for huge datasets\n\nThe functions that process big volumes of data, have an *Iterable/Stream* as argument.\nRAM should not get full!\n\n## It's well tested\n\nEvery function is tested for every language.\nTake a look at the test projects.\n\n## It's compatible across languages\n\nThe interface is similar across languages.\nYou can transfer a network from one platform to another via its **json instance**.\nCreate a neural network in *Python*, train it in *Java* and get its predictions in 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