Recurrent networks read a sequence one step at a time, which makes them slow to train and forgetful over long distances. Attention removes that bottleneck: instead of squeezing everything into one hidden state, each position looks directly at every other position and decides how much each one matters. Each token produces a query, a key and a value; queries are matched against keys to get attention weights, and those weights mix the values into a new, context-aware representation.
The transformer stacks this idea into a full architecture: multi-head attention so the model can track several relationships at once, positional encodings so word order is not lost, feed-forward layers, residual connections and normalization. Because every position is processed in parallel, transformers train efficiently on GPUs, which is why they power modern language models.
Watch the lessons in order, then continue to Pretraining & Fine-Tuning.
StatQuest introduces attention as a fix for the encoder-decoder bottleneck, showing step by step how a decoder can look back at every input word and weight each one.
Video 15min3Blue1Brown gives a visual overview of how a transformer turns text into tokens and embeddings, passes them through attention and feed-forward layers, and predicts the next word.
Video 27min3Blue1Brown works through a single attention head with queries, keys and values, then scales up to multi-head attention. The clearest visual walkthrough of the mechanism.
Video 26minStatQuest builds a transformer from word embeddings, positional encoding, self-attention and residual connections, using a small translation example.
Video 36minAndrej Karpathy codes a small GPT in PyTorch from an empty file, including self-attention and the transformer block. A long, hands-on session, best after the concept videos above.
Video 1h 56min