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opencog - A framework for integrated Artificial Intelligence & Artificial General Intelligence (AGI)

  •    Scheme

OpenCog is a framework for developing AI systems, especially appropriate for integrative multi-algorithm systems, and artificial general intelligence systems. Though much work remains to be done, it currently contains a functional core framework, and a number of cognitive agents at varying levels of completion, some already displaying interesting and useful functionalities alone and in combination. With the exception of MOSES and the CogServer, all of the above are in active development, are half-baked, poorly documented, mis-designed, subject to experimentation, and generally in need of love an attention. This is where experimentation and integration are taking place, and, like any laboratory, things are a bit fluid and chaotic.

gosom - Self Organizing Maps (SOM) in Go

  •    Go

This project provides an implementation of Self-Organizing Map (SOM) in Go. It implements the two most well known SOM training algorithms: sequential and batch. The batch training is faster than the sequential as it can be parallelized, taking advantage of as many cores as your machine provides. However it can be less accurate as it merely provides a resonable approximation of SOM, but still acceptable. The sequential algorithm is performed as its name implies, sequentially. Because of its sequential nature it's slower than batch training, but more accurate. You can read more about SOM training algorithms here. The goal of this project is to provide an API to build SOMs in Go. The project also implements various SOM quality measures which can help you validate the results of the training algorithm. In particular the project implements quantization and topographic error to measure both the projection and topography as well as topographic product which can help you make a decision about the size of the SOM grid.

goNEAT_NS - This project provides GOLang implementation of Neuro-Evolution of Augmented Topologies (NEAT) with Novelty Search optimization aimed to solve deceptive tasks with strong local optima

  •    Go

This repository provides implementation of Neuro-Evolution of Augmented Topologies (NEAT) with Novelty Search optimization implemented in GoLang. The Neuro-Evolution (NE) is an artificial evolution of Neural Networks (NN) using genetic algorithms in order to find optimal NN parameters and topology. Neuro-Evolution of NN may assume search for optimal weights of connections between NN nodes as well as search for optimal topology of resulting NN. The NEAT method implemented in this work do search for both: optimal connections weights and topology for given task (number of NN nodes per layer and their interconnections).

gosom - Self-organizing maps in Go

  •    Go

This project provides an implementation of Self-Organizing Map (SOM) in Go. It implements the two most well known SOM training algorithms: sequential and batch. The batch training is faster than the sequential as it can be parallelized, taking advantage of as many cores as your machine provides. However it can be less accurate as it merely provides a resonable approximation of SOM, but still acceptable. The sequential algorithm is performed as its name implies, sequentially. Because of its sequential nature it's slower than batch training, but more accurate. You can read more about SOM training algorithms here. The goal of this project is to provide an API to build SOMs in Go. The project also implements various SOM quality measures which can help you validate the results of the training algorithm. In particular the project implements quantization and topographic error to measure both the projection and topography as well as topographic product which can help you make a decision about the size of the SOM grid.









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