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Databytes on Instagram: "Gradient Descent can fail when an update step jumps outside the valid domain, a classic out of domain problem in univariate optimization. Large learning rates push parameters beyond allowed ranges, so boundary checks step control and advanced GD variants keep updates stable and meaningful. [out of domain gradient descent, univariate optimization, learning rate too high, update step overflow, boundary constraints, optimization failure, convex function limits, step size co
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LetsCodeAndAchieve on Instagram: "Gradient Descent| How AI Actually Learns . . What does it actually mean when we say an AI model is "learning"? It’s not magic—it’s math. Specifically, it’s Gradient Descent. In this video, we open the hood of the neural network engine. We explore how models use loss functions to measure error and how Gradient Descent iteratively updates weights to minimize that error. Key concepts covered: - The Loss Function: Measuring how "wrong" a model is. - The Gradient: Ca
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