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Recently I added back propagation algorithm over multilayer perceptron network.\nThe implementation is in dev. Planned features:\n\n- complete future features XD (see above)\n- find co-workers\n- create a ml library in openqasm (just kidding)\n- brainstorming / devtesting other an models\n\n### Updates\n\n2017-10-04: Introduced Recurrent Neural Network (Elman Network) with \"learn to sum integer\" task. Big refactoring in code (working on)\n\n2017-08-08: Introduced multi layer perceptron network definition with parametric number of hidden layer and neurons. Back propagation algorithm with different transfer function actived - I wanna thank you [dakk](https://github.com/dakk) because I was truly inspired by your code.\n\n2017-08-01: Introduced validation package and k-fold cross validation.\n\n2017-07-31: I started working on ```mlp``` branch for MLP + back prop. It doens't work yet but...I push first commit after some exp in dev. I delete ```dev``` because of some structs optimization.\n\n2017-07-31: we started working on k-fold validation.\n\n### Dependencies\n\n- [logrus](https://github.com/sirupsen/logrus)\n\n### Run test\n\nTo run a simple test just open a shell and run the following:\n\n```\ngit clone https://github.com/made2591/go-perceptron-go\ncd go-perceptron-go\ngo get github.com/sirupsen/logrus\ngo run main.go\n```\n\nYou can setup a MultiLayerPerceptron using ```PrepareMLPNet```. The first parameter, a simple ```[]int```, define the entire network struct. Example:\n\n- [4, 3, 3] will define a network struct with 3 layer: input, hidden, output, with respectively 4, 3 and 3 neurons. For classification problems the input layers has to be define with a number of neurons that match features of pattern shown to network. Of course, the output layer should have a number of unit equals to the number of class in training set.\nThe network will have this topology:\n\n![](./media/first_example.png)\n\n- [4, 6, 4, 3] will have this topology:\n\n![](./media/second_example.png)\n\n### To complete yet\n\n- test methods\n\n### Future features\n\n- [mathgl](https://github.com/go-gl/mathgl.git) for better vector space handling\n- some other cool neural model XD\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmade2591%2Fgo-perceptron-go","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmade2591%2Fgo-perceptron-go","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmade2591%2Fgo-perceptron-go/lists"}