Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in CNNs: Implementation in NumPy Backw

This video tutorial explains how to compute the derivative with respect to parameter K using the chain rule. It begins with a recap of previous concepts and introduces the necessary formulas. The tutorial then demonstrates the implementation of these calculations in code, focusing on defining functions, applying boundary conditions, and ensuring accurate computation for each matrix entry. The video concludes with a preview of the next topic, which involves computing the derivative with respect to parameter B.

What is the role of the helper function defined in the video?

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Explain how the derivative of the loss function is related to the computation of DK.

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What are the steps to compute the derivative with respect to K for each entry?

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What will be the next topic discussed in the following video?

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