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		<title>Predict coronavirus deaths by days</title>
		<link>https://petaminds.com/predict-coronavirus-deaths-by-days/</link>
					<comments>https://petaminds.com/predict-coronavirus-deaths-by-days/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Sun, 09 Feb 2020 09:52:42 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[visualization]]></category>
		<category><![CDATA[china]]></category>
		<category><![CDATA[corona]]></category>
		<category><![CDATA[covid-19]]></category>
		<category><![CDATA[death]]></category>
		<category><![CDATA[prediction]]></category>
		<category><![CDATA[regression]]></category>
		<category><![CDATA[virus]]></category>
		<category><![CDATA[wuhan]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=2182</guid>

					<description><![CDATA[<p>As the pandemic is going on with an increasing number of deaths daily, let create a simple model to predict the deaths caused by 2019-nCoV (Wuhan Coronavirus). The 2019-nCoV death data I grab the death toll data from World Meters website. Date Daily Deaths Feb. 8 89 Feb. 7 86 &#8230; &#8230; Jan. 24 16 [&#8230;]</p>
<p>The post <a href="https://petaminds.com/predict-coronavirus-deaths-by-days/">Predict coronavirus deaths by days</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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		<title>deep learning: Linear Autoencoder with Keras</title>
		<link>https://petaminds.com/deep-learning-linear-autoencoder-with-keras/</link>
					<comments>https://petaminds.com/deep-learning-linear-autoencoder-with-keras/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Mon, 09 Dec 2019 02:22:01 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[visualization]]></category>
		<category><![CDATA[auto]]></category>
		<category><![CDATA[decoder]]></category>
		<category><![CDATA[dimension]]></category>
		<category><![CDATA[encoder]]></category>
		<category><![CDATA[keras]]></category>
		<category><![CDATA[reduction]]></category>
		<category><![CDATA[tensorflow]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=2027</guid>

					<description><![CDATA[<p>This post introduces using linear autoencoder for dimensionality reduction using TensorFlow and Keras. What is a linear autoencoder An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”. Autoencoders consists [&#8230;]</p>
<p>The post <a href="https://petaminds.com/deep-learning-linear-autoencoder-with-keras/">deep learning: Linear Autoencoder with Keras</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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		<item>
		<title>Data Visualization &#8211; Charts with Matplotlib</title>
		<link>https://petaminds.com/data-visualization-matplotlib-python-chart/</link>
					<comments>https://petaminds.com/data-visualization-matplotlib-python-chart/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Thu, 07 Nov 2019 03:41:09 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[visualization]]></category>
		<category><![CDATA[data]]></category>
		<category><![CDATA[line]]></category>
		<category><![CDATA[matplotlib]]></category>
		<category><![CDATA[pair]]></category>
		<category><![CDATA[plot]]></category>
		<category><![CDATA[scatter]]></category>
		<category><![CDATA[stack]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=1738</guid>

					<description><![CDATA[<p>A common use for notebooks is data visualization using charts. It is easy with several charting tools available as Python imports. This article covers some common charts using matplotlib. Matplotlib Matplotlib&#160;is the most common charting package, see its&#160;documentation&#160;for details, and its&#160;examples&#160;for inspiration. Charting</p>
<p>The post <a href="https://petaminds.com/data-visualization-matplotlib-python-chart/">Data Visualization &#8211; Charts with Matplotlib</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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