Placeholder: At a local level, the modulus of continuity allows us to quantify the rate at which a function or sequence can change its values within a small interval. It tells us how much the function can deviate within a specific neighborhood, providing insights into its local behavior and fluctuations. On the other hand, when we consider the global perspective, the modulus of continuity provides information about the overall behavior of the function or sequence across a larger domain. It reveals how the fu At a local level, the modulus of continuity allows us to quantify the rate at which a function or sequence can change its values within a small interval. It tells us how much the function can deviate within a specific neighborhood, providing insights into its local behavior and fluctuations. On the other hand, when we consider the global perspective, the modulus of continuity provides information about the overall behavior of the function or sequence across a larger domain. It reveals how the fu

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At a local level, the modulus of continuity allows us to quantify the rate at which a function or sequence can change its values within a small interval. It tells us how much the function can deviate within a specific neighborhood, providing insights into its local behavior and fluctuations. On the other hand, when we consider the global perspective, the modulus of continuity provides information about the overall behavior of the function or sequence across a larger domain. It reveals how the fu

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Local and global approaches in mathematics and machine learning are both universal approximators, but they differ in the number of parameters required to represent a given function accurately. The entire system, including data, architecture, and loss function, must be considered, as they are interconnected. Data can be noisy or biased, architecture may demand excessive parameters, and the chosen loss function may not align with the desired goal. To address these challenges, practitioners should
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