K-Means algorithm parallelized in CUDA
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Updated
Nov 21, 2022 - Cuda
K-Means algorithm parallelized in CUDA
An API for managing chat completions, fine-tuning, payments, plans, and configurations.
This program implements the K-means clustering algorithm using OpenMP APIs. The K-means algorithm is a popular method of vector quantization that aims to partition n observations into k clusters. Each observation is assigned to the cluster with the nearest mean, serving as a prototype of the cluster.
In this Python notebook, we explore how K-Means can be used for customer segmentation to gain a competitive advantage and improve a business's bottom line.
This Machine Learning repository encompasses theory, hands-on labs, and two projects. Project 1 analyzes customer segmentation for marketing using clustering, while Project 2 applies supervised classification in marketing and sales.
This repo contains the Implementation of K-Means Clustering Algorithm from scratch and an Image Segmentation Project, implemented using the same algorithm.
Customer Segmentation using R
A C implementation of K-Means clustering algorithm with Python bindings
This Repo contains various Machine learning Algorithm including Linear regression, Logistic regression, Neural Networks, SVM, Clustering algorithms, K-means Algorithm, Anomaly detection, and Recommander system etc...
K-means algorithm is implemented from scratch for clustering on iris dataset and MNIST dataset.
Parallel-K-Means-Algorithm
A movie recommendation engine built with python and a Qt GUI.
An analysis using unsupervised Machine Learning algorithm to discover unknown patterns
In this project, I used unsupervised machine learning techniques to analyze cryptocurrency data.
This repository contains an example of using K-means clustering to partition data into distinct groups based on similarity.
The K -Means algorithm implementation from scratch in Python based on Euclidean distance
Cluster Visualization Tool
Unsupervised learning algorithms are used here. agglomerative algorithms and k-means clustering are used here.
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