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      <title>Explaining gradient descent in machine learning with a simple analogy</title>
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      <pubDate>Tue, 25 Jun 2024 00:00:00 GMT</pubDate>
      <dc:creator><![CDATA[innnotruong]]></dc:creator>
      <description><![CDATA[Gradient descent is a fundamental optimization algorithm in machine learning. It's a way for models to learn from data and improve their accuracy by gradually adjusting their internal settings. Think of it like carefully descending a hill to find the lowest point, each small step you take brings you closer to the best possible solution.]]></description>
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      <title>A grand unified theory of the AI hype cycle</title>
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      <pubDate>Thu, 13 Jun 2024 00:00:00 GMT</pubDate>
      <dc:creator><![CDATA[tieubao]]></dc:creator>
      <description><![CDATA[An exploration of the cyclical nature of AI development, tracing the rise and fall of new technologies within the field, and how this pattern has repeated throughout history.]]></description>
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      <title>Q learning</title>
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      <pubDate>Thu, 22 Jun 2023 00:00:00 GMT</pubDate>
      <dc:creator><![CDATA[thanhpn]]></dc:creator>
      <description><![CDATA[An introduction to Q-learning, a model-free reinforcement learning algorithm used to learn optimal policies in Markov Decision Processes.]]></description>
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