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

The video discusses the vanishing gradient problem, particularly in recurrent neural networks (RNNs), and its impact on long-term dependencies. It explains how the depth of RNNs, determined by the number of time steps, exacerbates this issue. The video also introduces the exploding gradient problem and its solution, gradient clipping. Finally, it outlines solutions to the vanishing gradient problem, including gated recurrent units (GRUs) and long short-term memory (LSTM) models.

What is a primary cause of the vanishing gradient problem in recurrent neural networks?

• The use of too many neurons

• The depth of the network due to time steps

• Overfitting the model

• Insufficient training data

How does the vanishing gradient problem affect long-term dependencies in recurrent neural networks?

• It enhances the network's memory

• It has no effect on the network

• It causes the network to forget long-term dependencies

• It improves the network's accuracy

What happens to the benefits of sequential modeling in the presence of vanishing gradients?

• They are enhanced

• They are doubled

• They remain unchanged

• They are lost

What is a key difference between the vanishing and exploding gradient problems?

• Vanishing gradients increase exponentially

• Exploding gradients decrease exponentially

• Vanishing gradients decrease exponentially, while exploding gradients increase exponentially

• Both problems result in the same gradient behavior

Which of the following is a simple solution to the exploding gradient problem?

• Gradient descent

• Gradient boosting

• Gradient clipping

• Gradient normalization

Why is it difficult to reduce the number of layers in recurrent neural networks?

• Because it would increase the computational cost

• Because it depends on the number of time steps

• Because it would decrease the model's accuracy

• Because it would require more data

What are two classical solutions to the vanishing gradient problem in recurrent neural networks?

• Data augmentation and regularization

• Convolutional layers and pooling

• Dropout and batch normalization

• Gated recurrent units and long short-term memory models