کتاب ترانسفورماتورها برای پردازش زبان طبیعی – ساخت، آموزش و تنظیم دقیق معماری‌های شبکه عصبی عمیق برای NLP با پایتون، PyTorch، TensorFlow، BERT و GPT-3 و موارد دیگر

Become an AI language understanding expert by mastering the quantum leap of Transformer neural network modelKey FeaturesBuild and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning modelsGo through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machineLearn training tips and alternative language understanding methods to illustrate important key conceptsThe Transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains in context with the Transformers.The book takes you through Natural language processing with Python and examines various eminent models and datasets in the transformer technology created by internet giants such as Google, Facebook, Microsoft, OpenAI, Hugging Face, and other contributors.The book trains you in three stages. The first stage introduces you to Transformer architectures, including RoBERTa, BERT, and DistilBERT Transformers with Hugging Face. You will discover training methods for smaller Transformers that can outperform GPT-3 in some cases. In the second stage, you will apply Transformers for Natural Language Understanding (NLU) and Generation. Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification.By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pre-trained transformer models by tech giants to various datasets.