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 in recurrent neural networks (RNNs), highlighting its impact on long-term dependencies and performance. It explains how the depth of RNNs, determined by time steps, exacerbates this issue. The exploding gradient problem is also introduced, with gradient clipping as a solution. The video concludes with an overview of solutions to the vanishing gradient problem, including Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) models.

What is the primary issue caused by the vanishing gradient problem in recurrent neural networks?

• Overfitting to training data

• Increased computational cost

• Loss of long-term dependencies

• Inability to process short sequences

How does the depth of a recurrent neural network relate to its input sequences?

• It is based on the number of time steps in the input sequences.

• It is equal to the number of neurons in the network.

• It is unrelated to the input sequences.

• It is determined by the number of layers in the network.

In the context of language modeling, what challenge does the vanishing gradient problem present?

• Difficulty in predicting the next word

• Loss of context over long sequences

• Inability to recognize punctuation

• Overemphasis on recent words

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

• Because it would lead to overfitting

• Because it would decrease the model's accuracy

• Because it depends on the number of time steps in the input

• Because it would increase the computational cost

What is the exploding gradient problem?

• Gradients that remain constant

• Gradients that increase exponentially

• Gradients that decrease exponentially

• Gradients that oscillate

Which method is commonly used to address the exploding gradient problem?

• Gradient normalization

• Gradient clipping

• Gradient descent

• Gradient boosting

What are the two classical solutions to the vanishing gradient problem mentioned in the video?

• Data augmentation and regularization

• Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTMs)

• Dropout and batch normalization

• Convolutional layers and pooling