Transformer Architectures

Transformer architectures refer to a type of neural network design introduced in 2017, primarily used for natural language processing tasks, which relies on self-attention mechanisms to weigh input data. As a key innovation in deep learning, transformer architectures have become a crucial component in many state-of-the-art language models, enabling advancements in tasks such as language translation, text generation, and question answering, and are being explored for applications beyond NLP, including computer vision and multimodal processing.