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		<title>ACOMEN2025</title>
		<link>https://dcn.nat.fau.eu/events/acomen2025/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Fri, 28 Feb 2025 19:15:37 +0000</pubDate>
				<category><![CDATA[conference]]></category>
		<category><![CDATA[Control]]></category>
		<category><![CDATA[Deep Learning]]></category>
		<category><![CDATA[DL]]></category>
		<category><![CDATA[PDE]]></category>
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					<description><![CDATA[<img width="1200" height="627" src="https://dcn.nat.fau.eu/wp-content/uploads/acomen2025_sep2025.png" class="attachment-post-thumbnail size-post-thumbnail wp-post-image" alt="" decoding="async" fetchpriority="high" srcset="https://dcn.nat.fau.eu/wp-content/uploads/acomen2025_sep2025.png 1200w, https://dcn.nat.fau.eu/wp-content/uploads/acomen2025_sep2025-300x157.png 300w, https://dcn.nat.fau.eu/wp-content/uploads/acomen2025_sep2025-1024x535.png 1024w, https://dcn.nat.fau.eu/wp-content/uploads/acomen2025_sep2025-768x401.png 768w" sizes="(max-width: 1200px) 100vw, 1200px" /> Next Fall on September 15 &#8211; 19, 2025 there is the 9th. international conference on &#8220;Advanced COmputational Methods in ENgineering and Applied Mathematics&#8221; (ACOMEN2025) in Ghent, Belgium. The conference aims to provide an international forum to present and discuss the latest developments in computational and applied mathematics in various emerging engineering and mathematical fields. The [&#8230;]]]></description>
		
		
		
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		<title>CIME summer school on PDEs, Control and Deep Learning</title>
		<link>https://dcn.nat.fau.eu/events/cime-summer-school-on-pdes-control-and-deep-learning/</link>
		
		<dc:creator><![CDATA[darlis.dcn]]></dc:creator>
		<pubDate>Mon, 20 Nov 2023 16:00:03 +0000</pubDate>
				<category><![CDATA[CIME]]></category>
		<category><![CDATA[Control]]></category>
		<category><![CDATA[Deep Learning]]></category>
		<category><![CDATA[DL]]></category>
		<category><![CDATA[PDE]]></category>
		<category><![CDATA[summer 2024]]></category>
		<category><![CDATA[Summer school]]></category>
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					<description><![CDATA[<img width="1200" height="627" src="https://dcn.nat.fau.eu/wp-content/uploads/CIMEsummerSchoolPDEsCtrlDL2024_flyer.png" class="attachment-post-thumbnail size-post-thumbnail wp-post-image" alt="" decoding="async" srcset="https://dcn.nat.fau.eu/wp-content/uploads/CIMEsummerSchoolPDEsCtrlDL2024_flyer.png 1200w, https://dcn.nat.fau.eu/wp-content/uploads/CIMEsummerSchoolPDEsCtrlDL2024_flyer-300x157.png 300w, https://dcn.nat.fau.eu/wp-content/uploads/CIMEsummerSchoolPDEsCtrlDL2024_flyer-1024x535.png 1024w, https://dcn.nat.fau.eu/wp-content/uploads/CIMEsummerSchoolPDEsCtrlDL2024_flyer-768x401.png 768w" sizes="(max-width: 1200px) 100vw, 1200px" /> This summer, Prof. Enrique Zuazua (Friedrich-Alexander-Universität Erlangen-Nürnberg) joint with Prof. Giuseppe M. Coclite (Politecnico di Bari) are organizing the &#8220;PDEs, Control and Deep Learning&#8221; Summer School on July 22-26, 2024 in Cetraro (Cosenza, Italy), supported by our Chair for Dynamics, Control, Machine Learning and Numerics &#8211; Alexander von Humboldt Professorship (FAU DCN-AvH) at FAU, Friedrich-Alexander-Universität [&#8230;]]]></description>
		
		
		
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		<title>Deep Learning and Paradigms</title>
		<link>https://dcn.nat.fau.eu/deep-learning-and-paradigms/</link>
		
		<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 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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