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DS52 Fundamentals of Data Science
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What happens when we increase the C parameter in soft-margin SVM?
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In the SVM dual formulation:
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ResNet models avoid overfitting mostly by:
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A Conv2D with kernel size 3x3, stride 1, and padding 1 applied on a 64x64 input gives:
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CLIP training uses:
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The experience replay buffer in Deep Q-Learning allows:
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In soft-margin SVM, the parameter C controls the trade-off between margin size and classification errors.
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The main objective of SVM is to find the hyperplane that maximizes the margin.
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For a polynomial kernel K(x,z) = (1 + x·z)² with x = (1,2) and z = (3,4), compute K(x,z):
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A valid kernel function must satisfy Mercer's condition (be positive semi-definite).
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