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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 use self‑attention, which lets every word look at all other words at the same time. Other options are incorrect because Many people think Transformers use recurrent layers to remember past words, but they do not; Some believe Transformers use convolutional layers to find local patterns, but they do not.
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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