Transformer Networks

Transformer networks are a type of deep learning architecture primarily used for processing sequential data, such as text. They utilize a mechanism called self-attention to weigh the significance of different words in a sentence, allowing them to capture long-range dependencies more effectively than previous models like RNNs. Transformers are characterized by their parallel processing capabilities, enabling faster training and inference times. Common use cases include natural language processing tasks such as translation, summarization, and sentiment analysis, as well as applications in image processing and beyond. They form the backbone of many state-of-the-art models, including BERT and GPT.