The post discusses Attention and The Transformer, essential concepts in modern deep learning models, focusing on neural machine translation. The Transformer, developed to speed up model training and outperform Google’s model, uses attention and parallelization effectively. The encoding and decoding components consist of multiple layers, including self-attention and feed-forward networks. Multi-headed attention allows the model to focus on different positions and representation subspaces. Positional encoding helps maintain the order of words in the input sequence. The decoder phase involves output generation, utilizing encoder-decoder attention for translation. The post provides detailed insights into the complex workings of The Transformer in a concise and informative manner.
https://jalammar.github.io/illustrated-transformer/