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      <title>AI &amp; Notion (공무길) by 공무길</title>
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      <description>GAMGAM</description>
      <language>en-us</language>
      <pubDate>2025-03-05 07:35:31 UTC</pubDate>
      <lastBuildDate>2025-03-05 07:35:32 UTC</lastBuildDate>
      <webMaster>hello@padlet.com</webMaster>
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      <item>
         <title>Essential Question</title>
         <author>emilmaxknauer</author>
         <link>https://padlet.com/kongmg1757/a9xfidreg0e19166/wish/3352083645</link>
         <description><![CDATA[What are Generative Adversarial Networks (GAM), and how do they work? Reflect on this question to guide the learning.]]></description>
         <enclosure url="https://www.youtube.com/watch?v=8L11aMN5KY8" />
         <pubDate>2025-03-05 07:35:31 UTC</pubDate>
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         <title>Direct Instruction</title>
         <author>emilmaxknauer</author>
         <link>https://padlet.com/kongmg1757/a9xfidreg0e19166/wish/3352083648</link>
         <description><![CDATA[The teacher explains the basic concepts of GAMs, focusing on the generator-discriminator duo and adversarial learning.]]></description>
         <enclosure url="https://www.youtube.com/watch?v=h45beyEeM1I" />
         <pubDate>2025-03-05 07:35:31 UTC</pubDate>
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         <title>Independent Practice</title>
         <author>emilmaxknauer</author>
         <link>https://padlet.com/kongmg1757/a9xfidreg0e19166/wish/3352083649</link>
         <description><![CDATA[Students experiment with a code notebook to train a simple GAN model. Adjust parameters and discuss the outcomes.]]></description>
         <enclosure url="https://subinium.github.io/VanillaGAN/" />
         <pubDate>2025-03-05 07:35:31 UTC</pubDate>
         <guid>https://padlet.com/kongmg1757/a9xfidreg0e19166/wish/3352083649</guid>
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      <item>
         <title>Practice Worksheet</title>
         <author>emilmaxknauer</author>
         <link>https://padlet.com/kongmg1757/a9xfidreg0e19166/wish/3352083650</link>
         <description><![CDATA[Complete a worksheet with problems focusing on GAM architecture and theory.]]></description>
         <enclosure url="https://www.iprep.online/courses/gan-aptitude-practice-test/" />
         <pubDate>2025-03-05 07:35:31 UTC</pubDate>
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      <item>
         <title>Key Skills</title>
         <author>emilmaxknauer</author>
         <link>https://padlet.com/kongmg1757/a9xfidreg0e19166/wish/3352083651</link>
         <description><![CDATA[Students will develop the ability to identify and explain the roles of the generator and discriminator in GAM. They will also understand how adversarial training improves the system.]]></description>
         <enclosure url="https://www.youtube.com/watch?v=ti_PCvND4gM" />
         <pubDate>2025-03-05 07:35:31 UTC</pubDate>
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         <title>Summative Assessment</title>
         <author>emilmaxknauer</author>
         <link>https://padlet.com/kongmg1757/a9xfidreg0e19166/wish/3352083652</link>
         <description><![CDATA[Students take a quiz assessing their understanding of GAM components, training techniques, and applications.]]></description>
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         <pubDate>2025-03-05 07:35:31 UTC</pubDate>
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      <item>
         <title>에브리씽</title>
         <author>emilmaxknauer</author>
         <link>https://padlet.com/kongmg1757/a9xfidreg0e19166/wish/3352083653</link>
         <description><![CDATA[]]></description>
         <enclosure url="https://www.perplexity.ai/" />
         <pubDate>2025-03-05 07:35:31 UTC</pubDate>
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      <item>
         <title>Reflection Time</title>
         <author>emilmaxknauer</author>
         <link>https://padlet.com/kongmg1757/a9xfidreg0e19166/wish/3352083655</link>
         <description><![CDATA[Ask students to summarize their understanding of GAMs, sharing reflections in groups.]]></description>
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         <pubDate>2025-03-05 07:35:31 UTC</pubDate>
         <guid>https://padlet.com/kongmg1757/a9xfidreg0e19166/wish/3352083655</guid>
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      <item>
         <title>Exit Ticket</title>
         <author>emilmaxknauer</author>
         <link>https://padlet.com/kongmg1757/a9xfidreg0e19166/wish/3352083657</link>
         <description><![CDATA[Students write one key takeaway about GAMs and its future implications for AI.]]></description>
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         <pubDate>2025-03-05 07:35:31 UTC</pubDate>
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