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
A fast units and dimensions library with support for static dimensionality checking and protobuffer serialization.
Comparison of various Dimensionality Reduction techiniques and Visualization of the same.
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.
Just a bunch of tools made in TypeScript.
Code for the paper, "The Curse of Dimensionality: Inside Out", DOI = 10.13140/RG.2.2.29631.36006.
montecarlo methods
Return the shape of a provided ndarray.
Minimal PCA library based on numpy and practical examples of dimensionality reduction use of the principal components in ETF market analysis.
Return the shape of a provided ndarray.
A Python project for generating and testing bipolar, multi-dimensional number sequences, representing scope and essence through layers of dimensions.
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.
RRLab is a growing collection of experimental techniques and quality-of-life tools born from years of hands-on data science, modeling, and exploration. Built as a personal lab bench in R, it reflects a constant search for better ways to understand high-dimensional data, test bold ideas, and streamline the grind of daily analysis.
Given a list of numbers in a file, estimates the dimensional expansivity of that dataset in a binary hamming space
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