Hi Pythonistas,
So far, we've learned the ingredients:
- Tokens
- Embeddings
- Attention
Now the question is: How do we stack these into a model that actually works?
The answer is the Transformer.Not a trick. Not a single idea.An architecture.
Why Transformers Exist
Older models (RNNs, LSTMs): Processed text step by step were slow and struggled with long context. Transformers flipped this completely.They process everything at once.No sequence bottleneck.No waiting for previous steps.
The Transformer Is a Stack.A Transformer is not one layer.It’s many identical layers stacked on top of each other.Each layer refines the representation.
Think of it like:
First layer: rough understanding
Middle layers: structure and relationships
Top layers: abstract patterns
Inside One Transformer Layer
Each layer has a simple rhythm:
- Self-Attention
- Feed-Forward Network
- Add & Normalize
That’s it.No magic just repetition at scale.Each token updates itself based on others.Multiple attention heads run in parallel.Each head learns a different "view" of the sequence.
Feed-Forward Network
After attention, each token goes through a small neural network.Same networkApplied independently to every tokenThis step Adds non-linearity.Mixes information inside each token. It’s like giving each token time to "think".
Residual Connections - Why Depth Works
Deep networks usually break.Transformers survive depth because of skip connections.Instead of replacing information:They add changes on top of the original signal.This keeps gradients healthy.Learning stays stable, even with dozens of layers.
Layer Normalization
We’ve seen normalization before.
Here it:
- Keeps values stable
- Prevents training chaos
- Helps gradients flow smoothly
Transformers rely heavily on this.
Encoder vs Decoder
Transformers come in two flavors:
Encoder → understands input
Decoder → generates output
What I Learned This Week
- Transformers process sequences in parallel
- Built from stacked layers
- Each layer = attention + feed-forward
- Skip connections keep learning stable
- Scale unlocks power
At this point, we have a full model.But it still doesn’t talk.
What's Coming Next
Next week we will learn about Autoregressive Generation