{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The Singular Value Decomposition (SVD)\n", "\n", "In this notebook we take a first look at one of the most important matrix decompositions, the SVD. The SVD gives what is arguably the \"best\" basis for the row and column spaces of a matrix, and reveals the \"true nature\" of a matrix in a unique way. It is heavily used in nearly all applications of linear algebra, especially to non-square matrices, especially for data analysis." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "application/vnd.webio.node+json": { "children": [], "instanceArgs": { "namespace": "html", "tag": "div" }, "nodeType": "DOM", "props": {}, "type": "node" }, "text/html": [ "
The WebIO Jupyter extension was not detected. See the\n", "\n", " WebIO Jupyter integration documentation\n", "\n", "for more information.\n", "