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        <title>vAI&#39;s Blog</title>
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        <description>Vaibhav&#39;s Blog on ML, DL, Programming and Statistics</description>
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    <title>Understanding Neural Networks from Geometric Perspective</title>
    <link>https://littlegiant-28.github.io/neural_network_intution/</link>
    <pubDate>Fri, 04 Apr 2025 10:38:08 -0500</pubDate>
    <author>Vaibhav Thakor</author>
    <guid>https://littlegiant-28.github.io/neural_network_intution/</guid>
    <description><![CDATA[For very deep neural networks, understanding their inner workings can be challenging. However, with shallow and low-dimensional networks, we can still analyze their behavior to some extent. In this blog, I will explain how neural networks perform pattern recognition from a geometric perspective. By visualizing how neural network weights interact with input data, we can gain insights into their decision-making process. I will illustrate this using various visualizations in coordinate space, primarily in 2D.]]></description>
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    <title>Monty Hall Problem</title>
    <link>https://littlegiant-28.github.io/monty_hall/</link>
    <pubDate>Fri, 14 Mar 2025 07:30:02 -0500</pubDate>
    <author>Vaibhav Thakor</author>
    <guid>https://littlegiant-28.github.io/monty_hall/</guid>
    <description><![CDATA[Let&rsquo;s say you are in a game show and there are 3 doors. Behind 2 doors there are goats and behind the remaining one door there is a car. You do not know what&rsquo;s behind which door and are asked to choose one door. Next, the host will reveal the door behind which there is a goat. Then the host asks you whether you want to switch your choice with the other closed door or not. Question is, Should you switch or not?]]></description>
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