Bio:

*Transformers for Natural Language Processing* (2nd Edition) by Denis Rothman provides a comprehensive guide to understanding and implementing transformer models for NLP tasks. The book covers the theory behind transformer architectures, including attention mechanisms, self-attention, and multi-head attention, and how they have revolutionized NLP. Rothman walks readers through the evolution of transformer models, from the original Transformer to BERT, GPT, and T5, offering practical implementation examples using popular deep learning frameworks like TensorFlow and PyTorch. The second edition updates the content to reflect the latest developments in the field, including advancements in large language models and fine-tuning techniques. It is suitable for both beginners and advanced practitioners who want to grasp the core concepts and applications of transformers in NLP.