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		<title>Sparse Matrices for Machine Learning quick note</title>
		<link>https://petaminds.com/sparse-matrices-for-machine-learning-quick-note/</link>
					<comments>https://petaminds.com/sparse-matrices-for-machine-learning-quick-note/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Mon, 02 Dec 2019 14:18:00 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[compressed]]></category>
		<category><![CDATA[matrix]]></category>
		<category><![CDATA[sparse]]></category>
		<category><![CDATA[triplet]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=1938</guid>

					<description><![CDATA[<p>In machine learning, many matrices are sparse. It is essential to know how to handle this kind of matrix. Sparse vs Dense Matrix First, it is good to know that sparse matrix looks similar to a normal matrix, with rows, columns or other indexes. But a sparse matrix is comprised of mostly zero (0s) values. [&#8230;]</p>
<p>The post <a href="https://petaminds.com/sparse-matrices-for-machine-learning-quick-note/">Sparse Matrices for Machine Learning quick note</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
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