Transformer Architecture

Transformer Architecture is a neural network design that revolutionized natural language processing by using self-attention mechanisms to process sequential data in parallel rather than sequentially. This architecture eliminates the need for recurrent or convolutional layers, enabling faster training and superior performance on language tasks. Transformers utilize multi-head attention mechanisms that allow models to focus on different parts of input sequences simultaneously, capturing long-range dependencies and contextual relationships more effectively than previous architectures. The framework consists of encoder and decoder blocks containing attention layers, feed-forward networks, and residual connections with layer normalization. Transformer architecture serves as the foundation for modern large language models including GPT, BERT, and T5, enabling breakthrough capabilities in text generation, translation, and comprehension. Advanced implementations incorporate positional encodings, attention optimization techniques, and scalable training methodologies.