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Transformers eliminate the need for recurrent layers, allowing for parallel processing.
The Transformer architecture requires convolutional layers to effectively handle sequence data.
Attention mechanisms enable Transformers to focus on relevant parts of the input sequence, improving context understanding.
The architecture's design allows for faster training times compared to traditional RNNs.
Transformers are less effective in handling long-range dependencies in sequences.
Understanding the Answer
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Transformers replace recurrent layers with self‑attention, so many parts of the input can be processed together. Other options are incorrect because Some people think transformers need convolutional layers to handle sequences; Another misconception is that transformers struggle with long‑range dependencies.
Key Concepts
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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