mnist

mnist_train

mnist_test

This is a 1% stratified random sample from the MNIST handwritten
digit data set.

datasets

Topological data analytic methods in machine learning rely on
vectorizations of the persistence diagrams that encode persistent
homology, as surveyed by Ali &al (2000)
<doi:10.48550/arXiv.2212.09703>. Persistent homology can be computed
using 'TDA' and 'ripserr' and vectorized using 'TDAvec'. The
Tidymodels package collection modularizes machine learning in R for
straightforward extensibility; see Kuhn & Silge (2022,
ISBN:978-1-4920-9644-3). These 'recipe' steps and 'dials' tuners make
efficient algorithms for computing and vectorizing persistence
diagrams available for Tidymodels workflows.

Jason Cory Brunson

tdarec

A 'recipes' Extension for Persistent Homology and Its
Vectorizations

mnist function

Two data frames of 600 and 100 rows, respectively, and 2 variables:<dl>
<dt><code>digit</code></dt>
<dd>list column of 28 × 28 numeric matrices</dd><dt><code>label</code></dt>
<dd>integer digit</dd>
</dl>

Format

MNIST handwritten digits — mnist

Two data frames of 600 and 100 rows, respectively, and 2 variables:<dl>
<dt><code>digit</code></dt>
<dd>list column of 28 × 28 numeric matrices</dd>

<dt><code>label</code></dt>
<dd>integer digit</dd>


</dl>

mnist: MNIST handwritten digits

Description

Usage

Arguments

Format