Why Transformers Replaced RNNs and LSTMs
For years, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks were the undisputed champions of sequential data processing. They powered state-of-the-art translation systems, voice assistants, and text generation models. However, in 2017, the seminal paper “Attention Is All You Need” (Vaswani et al.) introduced the Transformer architecture. Within a few years, RNNs and LSTMs were almost entirely phased out of mainstream AI models.
Transformers
RNN
LSTM
NLP
Deep Learning