Data Science and Machine Learning (Theory and Projects) A to Z - Vanishing Gradients in RNN: GRU Optional

The video tutorial explains the equations and mechanisms behind the Gated Recurrent Unit (GRU), focusing on its parameters, activation functions, and gate mechanisms. It covers the update and relevance gates, their roles, and how they help in managing information flow and memory retention in neural networks. The tutorial also highlights the importance of learnable parameters and how GRU addresses the vanishing gradient problem in recurrent neural networks.

What are the learnable parameters in the gated recurrent unit?

Describe how the gated recurrent unit addresses the vanishing gradient problem.

How does the gated recurrent unit differ from traditional recurrent neural networks?