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Related Questions
- What are the key components of a transformer model, and how do they contribute to its efficiency?
- How does the self-attention mechanism in transformers enable parallelization, reducing computational complexity?
- Can you explain the trade-offs between recurrent neural networks and transformers in terms of computational resources and model performance?
- What are the implications of transformer-based models on the scalability of deep learning applications?
- How do recurrent neural networks handle sequential data, and what are their limitations in this regard?
- What are some common applications where transformer-based models have shown significant performance improvements over RNNs?
- Can you provide a high-level comparison of the computational complexity of transformer-based models and RNNs in terms of time and space complexity?
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