Learning Path
Question & Answer
Choose the Best Answer
By using attention mechanisms that process all input tokens simultaneously
By reducing the number of layers in the network
By incorporating convolutional layers for better feature extraction
By sequentially processing tokens one at a time like RNNs do
Understanding the Answer
Let's break down why this is correct
Transformers use attention, a method that lets every word in a sentence talk to every other word at the same time. Other options are incorrect because Some think fewer layers would speed things up, but the number of layers mainly controls depth, not the speed of processing; Convolutional layers slide a small filter over the text, like looking at a tiny window at a time.
Key Concepts
Transformer Architecture
medium level question
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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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