Transformer Architectures

This chapter delves into transformer architectures, which have revolutionized natural language processing (NLP) and beyond. It covers the historical context, self-attention mechanisms, and the encoder–decoder structure of transformers. Popular transformer models like BERT and GPT are discussed, along with Vision Transformers (ViT and SWIN). The chapter addresses advanced topics such as transfer learning, efficient transformers, and interpretability. Practical applications in text generation, summarization, and question answering are provided, with implementation examples using TensorFlow and PyTorch.