Understanding Transformers: Why Attention Changed NLP

Before transformers

Sequence models used RNNs and later LSTMs. They were hard to parallelize and struggled with long-range dependencies.

The attention in idea

Attention lets every token look at every other token in one layer. That parallelism maps well to GPUs.

Why it matters

Transformers became the backbone of BERT, GPT, and most modern assistants. If you learn one architecture, learn this one.

Takeaway

Read the original "Attention Is All You Need" paper once, then build a tiny transformer in PyTorch to cement it.