This repository contains three projects developed as part of the "Methods of Computational Intelligence" course. Each project focuses on a selected aspect of computational intelligence, including Neural Networks, Kohonen Networks, and Genetic Algorithms. The aim is to explore these methods from scratch, understand their underlying principles, and apply them to solve various problems.
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Neural Networks from Scratch: An implementation of neural networks, designed to understand the fundamentals of neural computation, including forward propagation, backpropagation, and training mechanisms.
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Kohonen Networks from Scratch: Exploration of Kohonen Networks (Self-Organizing Maps), focusing on their ability to perform unsupervised learning and data visualization.
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Dive into Genetic Algorithms: A deep dive into Genetic Algorithms, showcasing their power in optimization problems, with a focus on selection, crossover, and mutation operations.
- /NeuralNetworks: Contains all files related to the Neural Networks project.
- /KohonenNetworks: Contains all files related to the Kohonen Networks project.
- /GeneticAlgorithms: Contains all files related to the Genetic Algorithms project.
- /data: Directory with datasets used in the projects.
- /*.pdf: PDF reports describing the process, findings, and conclusions of each project.
- requirements.txt: Lists all the dependencies required to run the projects.
- README.md: This file, providing an overview of the repository.
Ensure you have Python 3.8 or higher installed on your machine. All dependencies required to run the projects are listed in requirements.txt.
Clone the repository to your local machine:
git clone https://github.com/yourusername/methods-of-computational-intelligence.git