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Artificial-Intelligence

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Implementing Artificial Neural Network training process in Python

// repository documentation

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# Artificial-Intelligence

## [MLP](https://github.com/JuliaSzymanska/Artificial-Intelligence/tree/master/MLP)
This task consists of two parts, each of which presents one example of how to use the multilayer perceptron (MLP):
* [Approximation](https://github.com/JuliaSzymanska/Artificial-Intelligence/blob/master/MLP/Approximation.py)
* [Classification](https://github.com/JuliaSzymanska/Artificial-Intelligence/blob/master/MLP/Classification.py)

## [SOM](https://github.com/JuliaSzymanska/Artificial-Intelligence/tree/master/SOM)
The network implementing the classic self-organizing map consists of a set of neurons. Finding the optimal distribution of neurons in space is done in two ways:
* [Kohonen algorithm](https://github.com/JuliaSzymanska/Artificial-Intelligence/blob/master/SOM/KohonenMap.py)
* [Neural gas algorithm](https://github.com/JuliaSzymanska/Artificial-Intelligence/blob/master/SOM/NeuralGas.py)

## [Image compression](https://github.com/JuliaSzymanska/Artificial-Intelligence/tree/master/Image_compression)
Image quantization process carried out using self-organizing kohonen networks.

## [K-means](https://github.com/JuliaSzymanska/Artificial-Intelligence/tree/master/K-means)
Neural networks learning improvement using the k-means clustering algorithm.

## [RBF](https://github.com/JuliaSzymanska/Artificial-Intelligence/tree/master/RBF)
This task consists of two parts, each of which presents one example of how to use the neural network with radial base functions(RBF):
* [Approximation](https://github.com/JuliaSzymanska/Artificial-Intelligence/blob/master/RBF/Approximation.py)
* [Classification](https://github.com/JuliaSzymanska/Artificial-Intelligence/blob/master/RBF/Classification.py)

## License
[Apache License 2.0](https://github.com/JuliaSzymanska/Sudoku_Game/blob/master/LICENSE)

Artificial-Intelligence

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