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

This video tutorial delves into the details of a recurrent neural network, building on the previous setup and notations. It explains the structure of neurons and layers, including recurrent and output layers, and discusses the dimensions of feature vectors and outputs. The video covers the equations that govern the network's operations, including activation functions and biases. Finally, it focuses on parameter optimization using gradient descent to minimize loss, setting the stage for further exploration in subsequent videos.

Describe the process of applying gradient descent in the context of recurrent neural networks.

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How does the computation of Z1 differ from Z2 in a recurrent neural network?

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What is the importance of the chain rule in finding parameters in neural networks?

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