Question - How is the transformer architecture better than RNNs in Deep Learning?
Answer -
With the use of sequential processing, programmers were up against:
- The usage of high processing power
- The difficulty of parallel execution
This caused the rise of the transformer architecture. Here, there is a mechanism called attention mechanism, which is used to map all of the dependencies between sentences, thereby making huge progress in the case of NLP models.