The Skip Connection Is the Main Road
Last timePosition Without Recurrence
The residual path is usually described as a fix for vanishing gradients. In a transformer it is better understood as a shared channel that every block reads from and adds to.
Residual connections arrived in computer vision as a training fix. Very deep
networks were performing worse than shallow ones, and adding the input back to
the output of each block made depth usable again.
That history is accurate and it is also misleading when applied to transformers,
because it frames the skip connection as a repair. In a transformer the
residual path is better read the other way round: it is the main channel, and
the blocks are things attached to it.
The recurrence, unrolled
A transformer block does not transform its input. It computes something and adds
it:
Unroll that from the embedding:
The lesson stops here
7 more paragraphs to go
You have read the opening. The rest of the argument, the problems that check whether it landed, and the lines worth keeping at the end all come with a plan.
The first lesson of every course in the library reads the whole way through, free, so you can see exactly what the rest of them are.
See the planThe contentsThis is the reading half
Starting the course gives you your own copy of it. Every idea on every page has problems standing under it, marked with a reason rather than a tick, and any sentence you do not believe can be opened and argued with. None of that can happen on a page nobody owns.
The contents