Summary: Architecture of the Transformer Model

Get a quick recap of what we covered this chapter.

In this chapter, we first got started by examining the mind-blowing long-distance dependencies transformer architectures can uncover. Transformers can perform transductions from written and oral sequences to meaningful representations like never before in the history of Natural Language Understanding (NLU).

These two dimensions, the expansion of transduction and the simplification of implementation, are taking artificial intelligence to a level never seen before.

We explored the bold approach of removing RNNs, LSTMs, and CNNs from transduction problems and sequence modeling to build the transformer architecture. The symmetrical design of the standardized dimensions of the encoder and decoder makes the flow from one sublayer to another nearly seamless.

We saw that beyond removing recurrent network models, transformers introduce parallelized layers that reduce training time. We discovered other innovations, such as positional encoding and masked multi-headed attention.

The flexible, original transformer architecture provides the basis for many other innovative variations that open the way for even more powerful transduction problems and language modeling.

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