Podcast
Questions and Answers
What does the input to the transformer consist of?
What does the input to the transformer consist of?
- Tokenized words of the input sentence
- Token frequencies in the input text
- Sequence of embedding vectors (correct)
- Characters of the input text
How does the encoder enrich the embedding vectors?
How does the encoder enrich the embedding vectors?
- Through frequency analysis
- Through complex neural networks
- Through dimensionality reduction
- Through multiple encoder blocks (correct)
What is the purpose of the start-of-sentence marker?
What is the purpose of the start-of-sentence marker?
- To denote the importance of the sentence
- To separate sentences in a paragraph
- To indicate the end of a sentence
- To initiate the translation process (correct)
What is the processing pattern used by the decoder?
What is the processing pattern used by the decoder?
What is the output of the decoder?
What is the output of the decoder?
What is the role of positional encodings in the input embeddings?
What is the role of positional encodings in the input embeddings?
What is the primary purpose of the decoder in the transformer architecture?
What is the primary purpose of the decoder in the transformer architecture?
What is the dimension of the output vector from the decoder after the linear transformation?
What is the dimension of the output vector from the decoder after the linear transformation?
What is the purpose of the softmax layer in the transformer architecture?
What is the purpose of the softmax layer in the transformer architecture?
What is the purpose of the beam search approach?
What is the purpose of the beam search approach?
What is the greedy method used for in the transformer architecture?
What is the greedy method used for in the transformer architecture?
What is the main idea behind the paper title 'Attention Is All You Need'?
What is the main idea behind the paper title 'Attention Is All You Need'?
What is the input to the decoder after the first token is predicted?
What is the input to the decoder after the first token is predicted?
Why do we need to encode word positions in machine translation models?
Why do we need to encode word positions in machine translation models?
Where does the positional encoding happen in the transformer architecture?
Where does the positional encoding happen in the transformer architecture?
What is the dimensionality of the positional encoding in the base transformer?
What is the dimensionality of the positional encoding in the base transformer?
Why do we use element-wise addition to combine word embeddings and positional encodings?
Why do we use element-wise addition to combine word embeddings and positional encodings?
What is an alternative to element-wise addition for combining word embeddings and positional encodings?
What is an alternative to element-wise addition for combining word embeddings and positional encodings?
