<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"
	xmlns:content="http://purl.org/rss/1.0/modules/content/"
	xmlns:wfw="http://wellformedweb.org/CommentAPI/"
	xmlns:dc="http://purl.org/dc/elements/1.1/"
	xmlns:atom="http://www.w3.org/2005/Atom"
	xmlns:sy="http://purl.org/rss/1.0/modules/syndication/"
	xmlns:slash="http://purl.org/rss/1.0/modules/slash/"
	>

<channel>
	<title>matrix Archives - Petamind</title>
	<atom:link href="https://petaminds.com/tag/matrix/feed/" rel="self" type="application/rss+xml" />
	<link>https://petaminds.com/tag/matrix/</link>
	<description>A.I, Data and Software Engineering</description>
	<lastBuildDate>Tue, 05 Oct 2021 06:07:38 +0000</lastBuildDate>
	<language>en-US</language>
	<sy:updatePeriod>
	hourly	</sy:updatePeriod>
	<sy:updateFrequency>
	1	</sy:updateFrequency>
	<generator>https://wordpress.org/?v=6.9.4</generator>

<image>
	<url>https://petaminds.com/wp-content/uploads/2019/09/ic_launcher.png</url>
	<title>matrix Archives - Petamind</title>
	<link>https://petaminds.com/tag/matrix/</link>
	<width>32</width>
	<height>32</height>
</image> 
	<item>
		<title>Fast uniform negative sampling for rating matrix</title>
		<link>https://petaminds.com/fast-uniform-negative-sampling-for-rating-matrix/</link>
					<comments>https://petaminds.com/fast-uniform-negative-sampling-for-rating-matrix/#comments</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Fri, 21 Feb 2020 10:07:13 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[matrix]]></category>
		<category><![CDATA[negative sampling]]></category>
		<category><![CDATA[numpy]]></category>
		<category><![CDATA[python]]></category>
		<category><![CDATA[rating]]></category>
		<category><![CDATA[scipy]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=2250</guid>

					<description><![CDATA[<p>Sometimes, we want to reduce the training time by using a subset of a very large dataset while the negative samples outnumbers the positive ones, e.g. word embedding. Another situation when we deal with implicit data. In this case, we may need to populate new data for negative values. This post demonstrates how to generate [&#8230;]</p>
<p>The post <a href="https://petaminds.com/fast-uniform-negative-sampling-for-rating-matrix/">Fast uniform negative sampling for rating matrix</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
					<wfw:commentRss>https://petaminds.com/fast-uniform-negative-sampling-for-rating-matrix/feed/</wfw:commentRss>
			<slash:comments>1</slash:comments>
		
		
			</item>
		<item>
		<title>build a simple recommender system with matrix factorization</title>
		<link>https://petaminds.com/build-a-simple-recommender-system-with-matrix-factorization/</link>
					<comments>https://petaminds.com/build-a-simple-recommender-system-with-matrix-factorization/#comments</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Mon, 23 Dec 2019 23:13:25 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[factorization]]></category>
		<category><![CDATA[keras]]></category>
		<category><![CDATA[matrix]]></category>
		<category><![CDATA[recommender system]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=2075</guid>

					<description><![CDATA[<p>We will build a recommender system which recommends top n items for a user using the matrix factorization technique- one of the three most popular used recommender systems. matrix factorization Suppose we have a rating matrix of m users and n items. The rating of user to item is . Similar to PCA, matrix factorization [&#8230;]</p>
<p>The post <a href="https://petaminds.com/build-a-simple-recommender-system-with-matrix-factorization/">build a simple recommender system with matrix factorization</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
					<wfw:commentRss>https://petaminds.com/build-a-simple-recommender-system-with-matrix-factorization/feed/</wfw:commentRss>
			<slash:comments>3</slash:comments>
		
		
			</item>
		<item>
		<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>
]]></description>
		
					<wfw:commentRss>https://petaminds.com/sparse-matrices-for-machine-learning-quick-note/feed/</wfw:commentRss>
			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>TF2.0 Warm-up exercises (forked from @chipHuyen Repo)</title>
		<link>https://petaminds.com/tf2-0-warm-up-exercises-forked-from-chiphuyen-repo/</link>
					<comments>https://petaminds.com/tf2-0-warm-up-exercises-forked-from-chiphuyen-repo/#comments</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Fri, 08 Nov 2019 01:54:24 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[data]]></category>
		<category><![CDATA[exercise]]></category>
		<category><![CDATA[matrix]]></category>
		<category><![CDATA[tensorflow]]></category>
		<category><![CDATA[tf2]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=1681</guid>

					<description><![CDATA[<p>Heard of Ms @huyen chip for her notable yet controversial travelling books back in the day. I enjoy reading but I am not really into travel memoirs. Nevertheless, she did surprise everyone by her achievements by getting in Stanford, teaching TensorFlow, and then became a computer/data scientist. Her story is definitely very inspiring. For ones who [&#8230;]</p>
<p>The post <a href="https://petaminds.com/tf2-0-warm-up-exercises-forked-from-chiphuyen-repo/">TF2.0 Warm-up exercises (forked from @chipHuyen Repo)</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
					<wfw:commentRss>https://petaminds.com/tf2-0-warm-up-exercises-forked-from-chiphuyen-repo/feed/</wfw:commentRss>
			<slash:comments>4</slash:comments>
		
		
			</item>
	</channel>
</rss>
