In the previous article, we explored the use of self-attention layers, now we will dive into the final step of encoding and start moving into decoders
As the final step, we take the positional encoded values and add them to the self-attention values.
These connections are called residual connections. They make it easier to train complex neural networks by allowing the self-attention layer to focus on learning relationships between words, without needing to preserve the original word embedding and positional information.
At this point, we have everything needed to encode the input for this simple transformer.
These four components work together to convert words into meaningful numerical representations:
- Word embedding
- Positional encoding
- Self-attention
- Residual connections
Now that we have encoded the English input phrase “Let’s go”, the next step is to decode it into Spanish.
To do this, we need to build a decoder, which we will explore in the next article.
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