Transformers For Natural Language Processing Build Innovative Deep Neural Network Architectures For Nlp With Python Pytorch Tensorflow Bert Roberta And More Denis Rothman by Denis Rothman 9781800568631, 1800568630 instant download after payment.

Become an AI language understanding expert by mastering the quantum leap of Transformer neural network models

• Build 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 models
• Go through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machine
• Learn training tips and alternative language understanding methods to illustrate important key concepts

The 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 with transformers.

The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face.

The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. 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 Natural Language Generation (NLG). 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 pretrained transformer models by tech giants to various datasets.

• Use the latest pretrained transformer models
• Grasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer models
• Create language understanding Python programs using concepts that outperform classical deep learning models
• Use a variety of NLP platforms, including Hugging Face, Trax, and AllenNLP
• Apply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and more
• Measure the productivity of key transformers to define their scope, potential, and limits in production

Since the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers.

Readers who can benefit the most from this book include deep learning & NLP practitioners, data analysts and data scientists who want an introduction to AI language understanding to process the increasing amounts of language-driven functions.