Demonstration notebooks for Machine Learning

scGEAToolbox: Matlab toolbox for single-cell gene expression analyses

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.

DataHigh: A graphical user interface for visualizing and interacting with high-dimensional neural activity

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

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.

Return the shape of a provided ndarray.

montecarlo methods

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.

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.

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