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		<title>Deep Learning and Paradigms</title>
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		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 01 Mar 2021 13:53:16 +0000</pubDate>
				<category><![CDATA[Math]]></category>
		<category><![CDATA[Math Sergi Andreu]]></category>
		<category><![CDATA[Deep Learning]]></category>
		<category><![CDATA[Deep Neural Networks]]></category>
		<category><![CDATA[DL]]></category>
		<category><![CDATA[generalization]]></category>
		<category><![CDATA[Gradient descent]]></category>
		<category><![CDATA[neural network]]></category>
		<category><![CDATA[optimization]]></category>
		<category><![CDATA[Residual Neural Network]]></category>
		<category><![CDATA[ResNets]]></category>
		<category><![CDATA[stochastic properties]]></category>
		<category><![CDATA[target function]]></category>
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					<description><![CDATA[Deep Learning and Paradigms By Sergi Andreu // This post is the 2nd. part of the &#8220;Opening the black box of Deep Learning&#8221; post Deep Learning Now that we have some intuition about the data, it&#8217;s time to focus on how to approximate the functions that would fit that data. When doing *supervised learning*, we [&#8230;]]]></description>
		
		
		
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