Learning Path
Question & Answer
Choose the Best Answer
Input Embedding → Attention Mechanism → Output Decoding → Final Output
Attention Mechanism → Input Embedding → Final Output → Output Decoding
Input Embedding → Output Decoding → Attention Mechanism → Final Output
Output Decoding → Attention Mechanism → Input Embedding → Final Output
Understanding the Answer
Let's break down why this is correct
First the raw words turn into numeric vectors in the input embedding stage. Other options are incorrect because This option puts attention before the input has even been turned into numbers, so the model would have nothing to compare; It suggests decoding happens before attention, meaning the model would guess the answer before knowing how the words relate.
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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