<?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>data science Archives - Petamind</title>
	<atom:link href="https://petaminds.com/tag/data-science/feed/" rel="self" type="application/rss+xml" />
	<link>https://petaminds.com/tag/data-science/</link>
	<description>A.I, Data and Software Engineering</description>
	<lastBuildDate>Mon, 21 Feb 2022 20:37:56 +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>data science Archives - Petamind</title>
	<link>https://petaminds.com/tag/data-science/</link>
	<width>32</width>
	<height>32</height>
</image> 
	<item>
		<title>WHAT IS P-VALUE?</title>
		<link>https://petaminds.com/what-is-p-value/</link>
					<comments>https://petaminds.com/what-is-p-value/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Sat, 30 Jan 2021 00:43:00 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[math]]></category>
		<category><![CDATA[p-value]]></category>
		<category><![CDATA[statistics]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=2901</guid>

					<description><![CDATA[<p>In statistics, the p-value is the probability of obtaining results at least as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct. The p-value is used as an alternative to rejection points of significance at which the null hypothesis would be rejected. A smaller p-value means that there is stronger [&#8230;]</p>
<p>The post <a href="https://petaminds.com/what-is-p-value/">WHAT IS P-VALUE?</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
					<wfw:commentRss>https://petaminds.com/what-is-p-value/feed/</wfw:commentRss>
			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>Feature Engineering FundamentalS</title>
		<link>https://petaminds.com/feature-engineering-fundamentals/</link>
					<comments>https://petaminds.com/feature-engineering-fundamentals/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Tue, 22 Sep 2020 11:20:23 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[engineering]]></category>
		<category><![CDATA[feature]]></category>
		<category><![CDATA[one-hot]]></category>
		<category><![CDATA[research]]></category>
		<category><![CDATA[scaling]]></category>
		<category><![CDATA[split]]></category>
		<category><![CDATA[standardization]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=2714</guid>

					<description><![CDATA[<p>The features you use influence more than everything else the result. No algorithm alone, to my knowledge, can supplement the information gain given by correct&#160;feature engineering. — Luca Massaron What is a feature and why we need engineering of it? Basically, all machine learning algorithms use some input data to create outputs. This input data [&#8230;]</p>
<p>The post <a href="https://petaminds.com/feature-engineering-fundamentals/">Feature Engineering FundamentalS</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
					<wfw:commentRss>https://petaminds.com/feature-engineering-fundamentals/feed/</wfw:commentRss>
			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>Convolutional Neural Network with CIFAR and Tensorflow (example)</title>
		<link>https://petaminds.com/convolutional-neural-network-with-cifar-and-tensorflow-example/</link>
					<comments>https://petaminds.com/convolutional-neural-network-with-cifar-and-tensorflow-example/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Sat, 21 Sep 2019 00:53:10 +0000</pubDate>
				<category><![CDATA[back-end]]></category>
		<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[convolution]]></category>
		<category><![CDATA[tensorflow]]></category>
		<guid isPermaLink="false">http://petaminds.com/?p=613</guid>

					<description><![CDATA[<p>In this article, we assume that you already understand the basic concepts of a convolutional neural network (CNN), e.g. one-hot coding, convolution, pooling, fully-connected layer, activation functions. If you are totally new to these terms, please find and read our other articles. The problem We will use Tensorflow to build a model for classification of [&#8230;]</p>
<p>The post <a href="https://petaminds.com/convolutional-neural-network-with-cifar-and-tensorflow-example/">Convolutional Neural Network with CIFAR and Tensorflow (example)</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
					<wfw:commentRss>https://petaminds.com/convolutional-neural-network-with-cifar-and-tensorflow-example/feed/</wfw:commentRss>
			<slash:comments>0</slash:comments>
		
		
			</item>
		<item>
		<title>Bias vs Variance Quick note</title>
		<link>https://petaminds.com/bias-vs-variance-quick-note/</link>
					<comments>https://petaminds.com/bias-vs-variance-quick-note/#comments</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Mon, 28 May 2018 15:55:04 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[bias]]></category>
		<category><![CDATA[Machine learning]]></category>
		<category><![CDATA[overfitting]]></category>
		<category><![CDATA[prediction]]></category>
		<category><![CDATA[underfitting]]></category>
		<category><![CDATA[variance]]></category>
		<guid isPermaLink="false">http://petaminds.com/?p=524</guid>

					<description><![CDATA[<p>A proper understanding of bias and variance concepts would help us not only to build accurate models but also to avoid the mistake of over-fitting and under-fitting.  </p>
<p>The post <a href="https://petaminds.com/bias-vs-variance-quick-note/">Bias vs Variance Quick note</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
					<wfw:commentRss>https://petaminds.com/bias-vs-variance-quick-note/feed/</wfw:commentRss>
			<slash:comments>1</slash:comments>
		
		
			</item>
	</channel>
</rss>
