Transformer architecture
The Transformer architecture is a type of Neural Network design introduced in 2017 by Vaswani and his team, which revolutionized the field of Natural Language Processing by replacing traditional Recurrent Neural Networks and Convolutional Neural Networks with a novel, attention-based approach. This architecture relies on self-attention mechanisms to weigh the importance of different input elements relative to each other, allowing for more efficient and effective processing of sequential data, such as text or audio. The Transformer architecture has been widely adopted in various applications, including Machine Translation, Text Classification, and Speech Recognition. Its impact has also been felt in other areas, such as Computer Vision and Resource Management. The Transformer's ability to handle long-range dependencies and parallelize computation has made it a key component in many state-of-the-art models.