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Question & Answer
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Transformers can process sequences in parallel, allowing for faster training and improved efficiency.
Transformers rely on recurrent layers to capture long-term dependencies, similar to RNNs.
Transformers utilize convolutional layers to analyze local patterns in data.
Transformers require more computational resources than RNNs, making them less efficient.
Understanding the Answer
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Transformers can look at all words at the same time. Other options are incorrect because It sounds like Transformers use the same stepping‑by‑stepping idea of RNNs, but they do not; Some think Transformers use tiny filters to scan over words, but they do not use convolution.
Key Concepts
Transformer Architecture
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Deep Dive: Transformer Architecture
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Definition
The Transformer is a network architecture based solely on attention mechanisms, eliminating the need for recurrent or convolutional layers. It connects encoder and decoder through attention, enabling parallelization and faster training. The model has shown superior performance in machine translation tasks.
Topic Definition
The Transformer is a network architecture based solely on attention mechanisms, eliminating the need for recurrent or convolutional layers. It connects encoder and decoder through attention, enabling parallelization and faster training. The model has shown superior performance in machine translation tasks.
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