Question 1

1. What problem do transformers in deep learning solve that older models could not?

Accepted Answer

Transformers solve two key problems that RNNs and LSTMs struggled with: slow sequential processing and poor handling of long-range dependencies. By using attention instead of recurrence, transformers process all tokens in parallel and can relate any two tokens directly regardless of how far apart they are. This made training on large datasets practical for the first time.

Question 2

2. How much data do you need to train a transformer from scratch?

Accepted Answer

Training a large transformer from scratch requires billions of tokens of data. GPT-3 was trained on roughly 300 billion tokens. Smaller models like Phi-3 Mini showed that with very high-quality data, competitive results are possible with fewer tokens. In practice, most teams fine-tune existing pre-trained models rather than training from scratch, which requires far less data, sometimes just thousands of examples.

Question 3

3. What is the difference between self-attention and cross-attention?

Accepted Answer

Self-attention lets each token in a sequence attend to every other token in the same sequence. Cross-attention lets tokens in one sequence attend to tokens in a different sequence. In encoder-decoder transformers, the decoder uses cross-attention to look at the encoder's output while generating its own tokens. Self-attention handles within-sequence relationships. Cross-attention handles between-sequence relationships.

Question 4

4. Why do transformer models have so many parameters?

Accepted Answer

Each layer in a transformer contains multiple large weight matrices: the Q, K, V projections for each attention head, the feed-forward network weights, and the normalization parameters. With hundreds of layers and embedding dimensions in the thousands, parameter counts grow quickly. GPT-3 has 175 billion parameters. These large parameter counts are what allow the models to store broad world knowledge during pre-training.

Question 5

5. What is Flash Attention and why does it matter?

Accepted Answer

Flash Attention is an algorithm that makes the attention computation faster and more memory-efficient by reordering how it accesses GPU memory. Standard attention materializes the full attention matrix in memory, which is expensive for long sequences. Flash Attention computes attention in tiles without storing the full matrix, achieving the same result while using significantly less memory. This made longer context windows practical.

Question 6

6. Can transformers in deep learning work on tabular data?

Accepted Answer

Yes, though they are not always the best choice for tabular data. Models like TabTransformer and FT-Transformer apply transformer architectures to tabular features by treating each column as a token. They can outperform gradient boosting on some datasets, particularly when relationships between features are complex or when the dataset is large. For smaller tabular datasets, gradient boosting methods like XGBoost often still win.

Question 7

7. What is the role of layer normalization in a transformer?

Accepted Answer

Layer normalization stabilizes training by normalizing the activations within each layer so they have a consistent scale. Without it, deep transformers can suffer from unstable gradients and slow convergence. The original transformer applied layer norm after each sub-layer (post-norm). Most modern transformers apply it before each sub-layer (pre-norm), which makes training more stable, especially for very deep models.

Question 8

8. How do vision transformers handle images differently from CNNs?

Accepted Answer

CNNs use local filters that slide across an image, building up features through spatial locality and translation invariance. Vision transformers split an image into patches, treat each patch as a token, and apply global self-attention so every patch can relate to every other patch directly. This gives ViTs better ability to capture long-range spatial relationships but requires more data to train effectively compared to CNNs.

Question 9

9. What does "context window" mean for a transformer model?

Accepted Answer

The context window is the maximum number of tokens a transformer can process in a single forward pass. Everything inside the context window is available to the model's attention mechanism. Anything outside it is invisible. GPT-4 supports 128K tokens. Gemini 1.5 Pro supports up to 1 million tokens. A larger context window lets the model reason over longer documents, but increases memory and compute requirements significantly.

Question 10

10. What is the difference between fine-tuning and prompt engineering for transformers?

Accepted Answer

Prompt engineering means crafting better inputs to get better outputs from a pre-trained model without changing any weights. It is fast and cheap but limited by what the base model already knows. Fine-tuning actually updates the model's weights using task-specific data, changing how the model responds. Fine-tuning produces stronger results on specific tasks but requires data, compute, and more engineering effort.

Transformers in Deep Learning

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