Is Attention All We Need?
Learn how attention mechanisms solve the limitations of sequential models like RNNs and LSTMs by enabling parallel processing and preserving sequence relationships. This lesson explains the core ideas behind transformers and provides a basic coding example to illustrate how attention scores help generate context-aware outputs in deep learning.
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In 2017, Google took the concept of attention mechanisms to a whole new level. They moved away from traditional approaches, such as relying on recurrent connections, and instead embraced pure attention mechanisms.
The evolution of attention mechanisms
The sequence-to-sequence models we were familiar with used RNNs for encoding and decoding. As previously mentioned, these models faced fundamental issues. First, the hidden state of the final encoder lacked sufficient information. Moreover, they were slow in processing, as they depended on sequentially processing the input sequence to reach the final state before generating the first output token.
The challenge of sequential processing
Let's look at the encoder-decoder architecture to better understand the challenge of sequential processing.
For instance, all preceding tokens had to be processed sequentially to produce a single output token. This approach didn't fully leverage parallel processing capabilities, such as GPUs. In contrast, models like ConvNets for images exploit parallel processing efficiently. Consequently, sequential ...