Explain self-attention, LoRA, Adam vs SGD, ViT

Q: Explain self-attention, LoRA, Adam vs SGD, ViT

This question evaluates understanding of modern Machine Learning/Deep Learning topics, including self-attention mechanics (queries, keys, values and scaled logits), Low-Rank Adaptation (LoRA) for parameter-efficient fine-tuning and memory savings, optimizer behavior (Adam versus SGD with momentum), and architectural trade-offs between Vision Transformers and CNNs including patch-size considerations. It is categorized under Machine Learning and is commonly asked because it probes both conceptual understanding and practical application—testing reasoning about training dynamics, model scaling, fine-tuning strategies, and resource/performance trade-offs.

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Question

Answer the following ML/Deep Learning interview questions:

Describe self-attention in Transformer models. What are the queries, keys, and values, and how is the attention output computed?
Why are attention logits divided by $\sqrt{d_k}$ (where $d_k$ is the key/query dimension) before the softmax?
Describe LoRA (Low-Rank Adaptation) for fine-tuning large models. How does it modify the weight update during fine-tuning, and what are its main benefits?
Why does LoRA often reduce GPU memory consumption compared to full fine-tuning?
What is the difference between Adam and SGD (including SGD with momentum)? When might you prefer one over the other?
Compare Vision Transformers (ViT) and CNNs . What are the main pros and cons of each?
What factors influence the choice of ViT patch size (e.g., 8×8 vs 16×16 vs 32×32), and what are the trade-offs?

Explain self-attention, LoRA, Adam vs SGD, ViT

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