Attention Is All You Need — Transformer

Author(s): Sherwin Chen

Originally published on Towards AI.

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from https://wall.alphacoders.com/big.php?i=845641

Recurrent Neural Networks(RNNs), Long Short-Term Memory(LSTM) and Gated Recurrent Units(GRU) in particular, have been firmly established as state-of-the-art approaches in sequence modeling and transduction problems. Such models typically rely on hidden states to maintain historical information. They are beneficial in that they allow the model to make predictions based on useful historical information distilled in the hidden state. On the other hand, this inherently sequential nature precludes parallelization, which becomes critical at longer sequence lengths, as memory constraints limit batching across examples. Furthermore, in these models, the number of operations required to relate signals from two arbitrary input… Read the full blog for free on Medium.

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Published via Towards AI