https://github.com/juliaszymanska/artificial-intelligence
Implementing Artificial Neural Network training process in Python
https://github.com/juliaszymanska/artificial-intelligence
ai aproximation backtracking backtracking-algorithm classification color-quantization k-means kohonen kohonen-network kohonen-neural-network mlp neural-gas neural-network neuron neurons python radial-basis-function rbf sigmoid-activation sigmoid-function
Last synced: 17 days ago
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Implementing Artificial Neural Network training process in Python
- Host: GitHub
- URL: https://github.com/juliaszymanska/artificial-intelligence
- Owner: JuliaSzymanska
- License: apache-2.0
- Created: 2020-05-03T16:11:05.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-06-08T14:21:07.000Z (almost 5 years ago)
- Last Synced: 2025-03-26T13:21:23.299Z (about 1 month ago)
- Topics: ai, aproximation, backtracking, backtracking-algorithm, classification, color-quantization, k-means, kohonen, kohonen-network, kohonen-neural-network, mlp, neural-gas, neural-network, neuron, neurons, python, radial-basis-function, rbf, sigmoid-activation, sigmoid-function
- Language: Python
- Homepage:
- Size: 1.84 MB
- Stars: 15
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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)