Transparency

Transformer Networks are a type of deep learning architecture designed to handle sequential data efficiently. They utilize self-attention mechanisms to weigh the importance of different parts of the input simultaneously, allowing for better context understanding. This architecture enables parallel processing, improves performance on tasks like language translation and text generation, and has become fundamental in modern natural language processing.