My personal implementation of several unsupervised learning algorithms
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Updated
Jun 10, 2024 - Python
My personal implementation of several unsupervised learning algorithms
Self-Supervised Noise Embeddings (Self-SNE)
A single cell transcriptomics pipeline for QC, integration and making the data presentable
Functional Data Analysis Python package
An R package for detecting cell-to-cell variably methylated regions (VMRs) from single-cell bisulfite sequencing.
An intuitive visualization to understand mathamatical heavy dimension reduction algorithms
Clustering images of skin diseases using DINOv2 embeddings and dimensionality reduction techniques.
Exploring Cybersecurity Data Science: Dimensionality Reduction and Cluster Analysis
an interactive explorer for flow cytometry data
Manifold Learning via Diffusion Maps in Julia
TorchDR - PyTorch Dimensionality Reduction
Easy genetic ancestry predictions in Python
FeatureMAP (Feature-preserving Manifold Approximation and Projection) is an interpratable dimensionality reduction tool.
Feature Selection using Metaheuristics Made Easy: Open Source MAFESE Library in Python
This repository contains a Python implementation of Principal Component Analysis (PCA) for dimensionality reduction and variance analysis. PCA is a powerful statistical technique used to identify patterns in data by transforming it into a set of orthogonal (uncorrelated) components, ranked by the amount of variance they explain.
Project to demonstrate various clustering algorithms for customer segmentation.
Increase processing efficiency via principal components analysis
A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)
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