Transformers for Natural Language Processing: Build, train, and fine-tune deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, and GPT-3, Second Edition.

Revolutionize your natural language processing workflow with this comprehensive guide to building and training transformer models in Python, PyTorch, and TensorFlow. Learn how to leverage the power of BERT and GPT-3 for state-of-the-art NLP tasks such as text classification, sentiment analysis, and machine translation.

With expert guidance and hands-on tutorials, you'll develop a range of transformer architectures from scratch, including sequence-to-sequence models, attention-based models, and self-attention mechanisms. Explore the intricacies of transformer architecture and learn to fine-tune pre-trained models for your specific use case.

Discover how to:

Build custom transformer layers for improved performance
Train transformer models on large datasets with ease
Fine-tune BERT and GPT-3 for top-notch results

This second edition of Transformers for Natural Language Processing brings you the latest advancements in NLP research, from pre-trained models to advanced techniques. Whether you're a seasoned researcher or an NLP practitioner, this book will provide you with the knowledge and skills to tackle complex tasks and push the boundaries of language understanding.