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		<title>Backpropagation(BP) 倒傳遞法 #1 工作原理與說明</title>
		<link>https://www.brilliantcode.net/1326/backpropagation-1-gradient-descent-chain-rule/</link>
				<comments>https://www.brilliantcode.net/1326/backpropagation-1-gradient-descent-chain-rule/#comments</comments>
				<pubDate>Thu, 21 Feb 2019 14:04:15 +0000</pubDate>
		<dc:creator><![CDATA[Andy Wang]]></dc:creator>
				<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[教學]]></category>
		<category><![CDATA[Backpropagation]]></category>
		<category><![CDATA[Chain Rule]]></category>
		<category><![CDATA[Gradient Descent]]></category>
		<category><![CDATA[Optimization Algorithm]]></category>

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				<description><![CDATA[<p>本篇會介紹在機器學習（machine learning）與深度學習（deep learning）領域裡很流行的倒傳遞法（Back Propagation/ Backpropagation, BP）的精髓：梯度下降法（Gradient Descent）、連鎖率（Chain Rule）</p>
<p>這篇文章 <a rel="nofollow" href="https://www.brilliantcode.net/1326/backpropagation-1-gradient-descent-chain-rule/">Backpropagation(BP) 倒傳遞法 #1 工作原理與說明</a> 最早出現於 <a rel="nofollow" href="https://www.brilliantcode.net">BrilliantCode.net</a>。</p>
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