(DAY-1) Transformer: A Revolution in Sequence Modelling скачать в хорошем качестве

(DAY-1) Transformer: A Revolution in Sequence Modelling

the revolutionary shift in artificial intelligence triggered by the landmark paper, "Attention Is All You Need." Before this breakthrough, machine learning relied on sequential processing, which was slow and struggled to remember information across long distances. The Transformer architecture solved these issues by introducing self-attention, a mechanism that allows every part of a data sequence to connect with every other part simultaneously. By utilizing parallel processing, this new framework enabled the training of much larger models on massive datasets at unprecedented speeds. This innovation eventually led to the development of famous systems like GPT and BERT, proving its versatility across text, images, and audio. Ultimately, the sources illustrate how this transition from chain-like processing to global attention created the foundation for modern Generative AI. This provided for education purpose only Source: https://www.datacamp.com/tutorial/how... Source: https://research.google/blog/transfor...