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Yep, all of them: https://github.com/gorgonia/gorgonia
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I don't know that you necessarily need a neural net for what you are describing. A naive Bayes approach might do the trick without you needing to actually do any natural language recognition. A neural net might actually be harder to tune because of the sheer number of inputs you will have if each possible word is an input. I played around with naive Bayes for a toy SMS spam classifier: https://github.com/PaluMacil/ham
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If you do decide you want to play with a neural net, you can certainly write one in pure Go. Doing the training is going to be pretty inefficient because as everyone else said, utilizing graphics cards or simply standing on the shoulders of all the work done before you requires talking to C or C++ but often you only need to train once or at least offline on occasion. When you're only talking about feeding forward to classify with no back propagation (the training where where the math gets heavier), it's actually not a very expensive calculation. I have another toy sized example here: https://github.com/PaluMacil/gophernet