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		<title>Perceptrons, Neural Networks and Dynamical Systems</title>
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		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Wed, 24 Mar 2021 19:02:27 +0000</pubDate>
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					<description><![CDATA[Perceptrons, Neural Networks and Dynamical Systems Perceptrons, Neural Networks and Dynamical Systems Sergi Andreu, DeustoCCM // This post is last part of the &#8220;Deep Learning and Paradigms&#8221; post Binary classification with Neural Networks When dealing with data classification, it is very useful to just assign a color/shape to every label, and so be able to visualize data in a [&#8230;]]]></description>
		
		
		
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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>
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		<category><![CDATA[Deep Learning]]></category>
		<category><![CDATA[Deep Neural Networks]]></category>
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		<category><![CDATA[generalization]]></category>
		<category><![CDATA[Gradient descent]]></category>
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		<category><![CDATA[optimization]]></category>
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					<description><![CDATA[Deep Learning and Paradigms Sergi Andreu, DeustoCCM // 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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		<title>Opening the black box of Deep Learning</title>
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		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Wed, 24 Feb 2021 22:17:10 +0000</pubDate>
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		<category><![CDATA[Deep Learning]]></category>
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					<description><![CDATA[Opening the black box of Deep Learning Sergi Andreu, DeustoCCM Deep Learning is one of the three main paradigms of Machine Learning, and roughly consists on extracting patterns from data using neural networks. Its impact in modern technologies is huge. However, there is not a clear high-level description of what these algorithms are actually doing. [&#8230;]]]></description>
		
		
		
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