A notebook benchmarking a recently developed Dimensionality Reduction technique using Siamese Networks supporting both supervised and unsupervised modes.
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Updated
Jul 31, 2019 - Jupyter Notebook
A notebook benchmarking a recently developed Dimensionality Reduction technique using Siamese Networks supporting both supervised and unsupervised modes.
Boolean parameterization maps using UMAP
Visualisation exploring handwriting styles using convolutional autoencoders and clustering
This repository contains code used to create Paper "Navigating Complexity: Evaluating the Effectiveness of Dimension Reduction and Clustering Approaches on Challenging Datasets" as a Bachelor's thesis for EUR.
UMAP-enabled 2D representation of the TFcheckpoint protein dataset
Repository for fine-tuning and clustering sentence embeddings for Food Items
Recommender system predicting user beer preferences.
Public data for the Embedding Projector
En este repositorio se encuentra el Trabajo de Fin de Máster de la Titulación Máster en Ciencia de Datos por la Universitat Oberta de Catalunya.
Dimensionality Reduction and clustering + interprability
Repository dedicated to my project on PhenoGraph.
Examining data similarities across stock/commodity/money markets. Reducing dimensionality via UMAP, applying stochastic processes to the results.
Topic modelling of invoice data
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