My talk to UFRJ Ecology Graduate Program
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Updated
Sep 15, 2021 - HTML
My talk to UFRJ Ecology Graduate Program
Dimensionality reduction is the process of reducing the number of features or dimensions in a dataset. This can be useful for reducing the complexity of a dataset and making it easier to work with.
Given a list of numbers in a file, estimates the dimensional expansivity of that dataset in a binary hamming space
Return the shape of a provided ndarray.
montecarlo methods
Minimal PCA library based on numpy and examples of practical dimensionality reduction use of the principal components in ETF market analysis.
Return the shape of a provided ndarray.
Code for the paper, "The Curse of Dimensionality: Inside Out", DOI = 10.13140/RG.2.2.29631.36006.
Just a bunch of tools made in TypeScript.
Visualization and embedding of large datasets using various Dimensionality Reduction (DR) techniques such as t-SNE, UMAP, PaCMAP & IVHD. Implementation of custom metrics to assess DR quality with complete explaination and workflow.
Comparison of various Dimensionality Reduction techiniques and Visualization of the same.
DataHigh: A graphical user interface for visualizing and interacting with high-dimensional neural activity
The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs onto a non-uniform parameter/time-varying grid, such that the Kolmogorov n-width of the mapped data on the learned grid is minimized.
scGEAToolbox: Matlab toolbox for single-cell gene expression analyses
Demonstration notebooks for Machine Learning
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