Transformers: the evolution in neural networks after self-attention
Our exploration into self-attention revealed its powerful capability to reinterpret sequence data, providing each word with a contextual understanding based on its relationships with other words. This principle set the stage for an evolutionary leap in neural network designs: the transformer architecture.
Introduced by the Google Brain team in their 2017 paper, Attention is All You Need (https://arxiv.org/abs/1706.03762), the transformer architecture is built upon the very essence of self-attention. Before its advent, RNNs were the go-to. Picture RNNs as diligent librarians reading an English sentence to translate it into German, word by word, ensuring the context is relayed from one word to the next. They’re reliable for short texts but can stumble when sentences get too long, misplacing the essence of earlier words.
Figure 11.7: Encoder-decoder architecture of the original transformer
Transformers...
