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Related Questions
- What is the key difference between self-attention and RNNs in handling long-range dependencies in sequential data?
- How do transformers overcome the limitations of RNNs in capturing long-range dependencies?
- Can you explain the concept of positional encoding in transformers and its role in handling long-range dependencies?
- How does the self-attention mechanism in transformers allow for parallelization, enabling efficient handling of long-range dependencies?
- What are the advantages of using transformers over RNNs for sequential data with long-range dependencies?
- Can you provide an example of a scenario where transformers outperform RNNs in handling long-range dependencies?
- How do the parallelization capabilities of transformers compare to the sequential processing of RNNs in terms of handling long-range dependencies?
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