❓ Interview question :
What is the Transformer architecture, and why is it considered a breakthrough in NLP?

❓ Interview question :
How does self-attention enable Transformers to capture long-range dependencies in text?

❓ Interview question :
What are the main components of a Transformer model?

❓ Interview question :
Why are positional encodings essential in Transformers?

❓ Interview question :
How does multi-head attention improve Transformer performance compared to single-head attention?

❓ Interview question :
What is the purpose of feed-forward networks in the Transformer architecture?

❓ Interview question :
How do residual connections and layer normalization contribute to training stability in Transformers?

❓ Interview question :
What is the difference between encoder and decoder in the Transformer model?

❓ Interview question :
Why can Transformers process sequences in parallel, unlike RNNs?

❓ Interview question :
How does masked self-attention work in the decoder of a Transformer?

❓ Interview question :
What is the role of key, query, and value in attention mechanisms?

❓ Interview question :
How do attention weights determine which parts of input are most relevant?

❓ Interview question :
What are the advantages of using scaled dot-product attention in Transformers?

❓ Interview question :
How does position-wise feed-forward network differ from attention layers in Transformers?

❓ Interview question :
Why is pre-training important for large Transformer models like BERT and GPT?

❓ Interview question :
How do fine-tuning and transfer learning benefit Transformer-based models?

❓ Interview question :
What are the limitations of Transformers in terms of computational cost and memory usage?

❓ Interview question :
How do sparse attention and linear attention address scalability issues in Transformers?

❓ Interview question :
What is the significance of model size (e.g., number of parameters) in Transformer performance?

❓ Interview question :
How do attention heads in multi-head attention capture different types of relationships in data?

#️⃣ tags: #Transformer #NLP #DeepLearning #SelfAttention #MultiHeadAttention #PositionalEncoding #FeedForwardNetwork #EncoderDecoder

By: t.iss.one/DataScienceQ 🚀