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    <title>Overfitting on Chandras Edu | AI, US Stocks, Courses &amp; Jobs India</title>
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      <title>Remarks on the Backpropagation Algorithm</title>
      <link>https://chandrashaker.in/courses/paiml/unit4/remarks-backpropagation/</link>
      <pubDate>Mon, 01 Jun 2026 00:00:00 +0000</pubDate>
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      <description>&lt;h2 id=&#34;unit-4-navigation&#34;&gt;Unit 4 Navigation&lt;/h2&gt;&#xA;&lt;p&gt;&lt;strong&gt;← &lt;a href=&#34;https://chandrashaker.in/courses/paiml/unit4/backpropagation/&#34;&gt;Multilayer Networks and Backpropagation&lt;/a&gt;&lt;/strong&gt;&#xA; | &#xA;&lt;strong&gt;Next → &lt;a href=&#34;https://chandrashaker.in/courses/paiml/unit4/face-recognition/&#34;&gt;An Example: Face Recognition&lt;/a&gt;&lt;/strong&gt;&lt;/p&gt;&#xA;&lt;hr&gt;&#xA;&lt;h2 id=&#34;461-convergence-and-local-minima&#34;&gt;4.6.1 Convergence and Local Minima&lt;/h2&gt;&#xA;&lt;h3 id=&#34;the-local-minima-problem&#34;&gt;The Local Minima Problem&lt;/h3&gt;&#xA;&lt;p&gt;Unlike the linear unit — which has a single smooth bowl-shaped error surface — multilayer network error surfaces may have &lt;strong&gt;many local minima&lt;/strong&gt;. Backpropagation is therefore only guaranteed to converge to &lt;strong&gt;some local minimum&lt;/strong&gt;, not necessarily the global one.&lt;/p&gt;&#xA;&lt;pre tabindex=&#34;0&#34;&gt;&lt;code&gt;    E(w)&#xA;     │         Local    Global&#xA;     │    Local Min     Min&#xA;     │    Max  ↓   \   /&#xA;     │   / \  / \  /\_/&#xA;     │  /   \/   \/&#xA;     └──────────────────── w&#xA;&lt;/code&gt;&lt;/pre&gt;&lt;p&gt;Despite this, Backpropagation works well in practice. Local minima are less severe than expected for two reasons:&lt;/p&gt;</description>
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