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

The video tutorial explains the application of the chain rule in computing the gradient of the loss function with respect to different variables, focusing on time step T. It details how the variable A impacts the loss L through Z2, and how to compute the derivative of L with respect to Z2. The tutorial also covers the computation of gradients with respect to parameters WX and WY, and concludes with a preview of handling time step T-1 in the next video.

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