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	<title>loss function Archives - Petamind</title>
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	<title>loss function Archives - Petamind</title>
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	<item>
		<title>Advanced Keras &#8211; Custom loss functions</title>
		<link>https://petaminds.com/advanced-keras-custom-loss-functions/</link>
					<comments>https://petaminds.com/advanced-keras-custom-loss-functions/#comments</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Wed, 23 Mar 2022 00:31:00 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[cost function]]></category>
		<category><![CDATA[custom loss]]></category>
		<category><![CDATA[K]]></category>
		<category><![CDATA[keras]]></category>
		<category><![CDATA[keras backend]]></category>
		<category><![CDATA[loss function]]></category>
		<category><![CDATA[neural network]]></category>
		<category><![CDATA[tensorflow]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=1391</guid>

					<description><![CDATA[<p>When working on machine learning problems, sometimes you want to construct your own custom loss function(s). This article will introduce abstract Keras backend for that purpose. Keras loss functions From Keras loss documentation, there are several built-in loss functions, e.g. mean_absolute_percentage_error, cosine_proximity, kullback_leibler_divergence etc. When compiling a Keras model, we often pass two parameters, i.e. [&#8230;]</p>
<p>The post <a href="https://petaminds.com/advanced-keras-custom-loss-functions/">Advanced Keras &#8211; Custom loss functions</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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		<title>Common Loss functions and their uses &#8211; quick note</title>
		<link>https://petaminds.com/common-loss-functions-and-their-use-quick-note/</link>
					<comments>https://petaminds.com/common-loss-functions-and-their-use-quick-note/#respond</comments>
		
		<dc:creator><![CDATA[Tung Nguyen]]></dc:creator>
		<pubDate>Sat, 08 Feb 2020 02:28:54 +0000</pubDate>
				<category><![CDATA[data science]]></category>
		<category><![CDATA[math]]></category>
		<category><![CDATA[Project]]></category>
		<category><![CDATA[Research]]></category>
		<category><![CDATA[loss function]]></category>
		<category><![CDATA[Machine learning]]></category>
		<guid isPermaLink="false">https://petaminds.com/?p=2132</guid>

					<description><![CDATA[<p>Machines learn by means of a loss function which reflects how well a specific model performs with the given data. If predictions deviate too much from actual results, loss function would yield a very large value. Gradually, with function, parameters are modified accordingly to reduce the error in prediction. In this article, we will quickly [&#8230;]</p>
<p>The post <a href="https://petaminds.com/common-loss-functions-and-their-use-quick-note/">Common Loss functions and their uses &#8211; quick note</a> appeared first on <a href="https://petaminds.com">Petamind</a>.</p>
]]></description>
		
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