This project provides a comprehensive guide to implementing PCA from scratch and validating it using scikit-learn's implementation. The visualizations help in understanding the data's variance and the effectiveness of dimensionality reduction.

Python package containing the tools necessary for radial basis function (RBF) applications

Polyharmonic spline interpolation in PyTorch

2nd Project of Course 'Machine Learning' of the SMARTNET programme. Taken at the National and Kapodistrian University of Athens.

MITx - MicroMasters Program on Statistics and Data Science - Machine Learning with Python - Second Project

Python Package for Radial Basis Function Networks

An RBF network implementation for interpolation

I trained an RBF Neural Network for function approximation.

This repository contains all program files and datasets used in implementation of Masters Thesis Research Work for the topic - "Efficient Clustering via Kernel Principal Component Analysis and Optimal One Dimensional Clustering".

Spectral clustering, RBF kernels, and hyperparameter optimization on non-radial data are used to cluster data that gives traditional k-means difficulty.

A Python implementation of the Faul-Goodsen-Powell algorithm for multidimensional interpolation with radial basis functions.

Radial Basis Function (RBF) network implementation from scratch for one input variable, one output variable.

This project implements classification using a Multi-Layer Perceptron (MLP) trained via backpropagation and a Radial Basis Function (RBF) Network with random center selection. Performance comparison includes decision boundary visualization and accuracy analysis.

Collection of neural network implementations done from scratch.

Real-time magnetic field mapping and interpolation.

Python Implementation of the preconditioned conjugate gradient algorithm

A small collection of functions associated with radial basis function interpolation and collocation

Implementation of Radial basis function network

Comparison of two algorithms for hyperparameters global optimization: Radial Basis Functions and Bayesian gaussian processes.

HydroWizard: State-of-the-art hydrological modeling, optimization, and simulation using neural networks, genetic algorithms, and DAGs.