Demonstration notebooks for Machine Learning

scGEAToolbox: Matlab toolbox for single-cell gene expression analyses

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.

A fast units and dimensions library with support for static dimensionality checking and protobuffer serialization.

Minimal PCA library based on numpy and practical examples of dimensionality reduction use of the principal components in ETF market analysis.

Comparison of various Dimensionality Reduction techiniques and Visualization of the same.

A Python project for generating and testing bipolar, multi-dimensional number sequences, representing scope and essence through layers of dimensions.

Code for the paper, "The Curse of Dimensionality: Inside Out", DOI = 10.13140/RG.2.2.29631.36006.

My talk to UFRJ Ecology Graduate Program

Just a bunch of tools made in TypeScript.

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.

montecarlo methods

Given a list of numbers in a file, estimates the dimensional expansivity of that dataset in a binary hamming space

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.

Return the shape of a provided ndarray.

Return the shape of a provided ndarray.