Data Science and Machine Learning (Theory and Projects) A to Z - Gradient Descent in RNN: Chain Rule

The video tutorial explains the importance of gradients in minimizing the loss function and updating parameters. It introduces the concept of gradient calculation, particularly focusing on the derivative of the loss function with respect to WX. The tutorial further breaks down the gradient calculation using the chain rule, simplifying complex calculations into manageable parts. The application of the chain rule is demonstrated, emphasizing the simplification of gradient calculations through progressive breakdown into smaller problems.

What are the steps involved in calculating the derivative of Z1 with respect to X?

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In what way does the complexity of the gradient computation change as you apply the chain rule?

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What is the significance of the activation function in the computation of gradients?

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