Implementing Artificial Neural Network training process in Python

Assignments on Neural Networks

Unsupervised clustering for the UCI-WINE dataset using Kohonen Network

Module 4 of the course IT-3105 Artificial intelligence programming at NTNU. Self organizing maps are based on unsupervised, competitive learning. For this project, the neural network is structured after the "Kohonen network".

🌐 🧠 This project is an implementation of a self-organising map.

Unsupervised learning implementations in Python including PCA, Kohonen, Oja and Hopfield.

📘 dimensionality reduction algorithms

Self-Organizing Map (Kohonen Self-Organizing Feature Map)

A Self Organizing Maps (SOM) or Kohonen Network is a type of Artificial Neural Network that is trained using clustering of datasets. This repo implements SOM using MiniSOM library applied on Iris Dataset and outputs the confusion matrix and clustering accuracy

This repository consists of codes regarding different neural network algorithom implementation.

Deep Neural Networks from scratch

86.54 - Basic concepts of neural networks. Hopfield Networks, Ising Model, Simple-Layer Perceptron, Multi-Layer Perceptron, Genetic Algorithms, Kohonen Networks, Simulated Annealing.

Time series forecasting using Neural Networks

Classification project using Self-Organizing Maps (SOM) to differentiate patients and healthy subjects from marker data, encompassing network construction, training, and testing phases.

Library for usage different neuron networks and combine them.

This program implements SOM network and includes amazing visualizations