A common method to combat the vanishing gradient problem in RNNs is to use _______.
- Gradient boosting
- Long Short-Term Memory (LSTM)
- Principal Component Analysis
- K-means clustering
To combat the vanishing gradient problem in RNNs, a common approach is to use Long Short-Term Memory (LSTM) units. LSTMs are designed to alleviate the vanishing gradient issue by allowing gradients to flow over longer sequences.
Loading...
Related Quiz
- What is the main challenge addressed by the transformer architecture in NLP?
- The _______ is a measure of the relationship between two variables and ranges between -1 and 1.
- RNNs are particularly effective for tasks like _______ because they can retain memory from previous inputs in the sequence.
- In computer vision, detecting specific features or patterns in an image is often achieved using _______.
- Which type of recommender system suggests items based on a user's past behavior and not on the context?