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 is the primary focus of the video regarding Gated Recurrent Units (GRU)?

• Pictorial representation of GRU

• History of GRU

• Applications of GRU

• Equations behind GRU

What are the parameters in GRU primarily used for?

• Enhancing visual representation

• Error correction

• Matrix multiplication and activation functions

• Data storage

Which activation function is typically used in GRU?

• Tanh

• Softmax

• Sigmoid

• ReLU

What determines whether the candidate activations become the current activations in GRU?

• The number of layers

• The size of the input data

• The value of the update gate

• The learning rate

What is the role of the update gate in GRU?

• To decide between candidate and previous activations

• To adjust learning rate

• To store previous activations

• To initialize weights

What is the purpose of the relevance gate in GRU?

• To initialize biases

• To store input data

• To determine the relevance of current activations

• To enhance the learning rate

Which activation function is used for the relevance gate in GRU?

• Tanh

• Sigmoid

• Softmax

• ReLU