Packt - Transformers For Natural Language Processing Build Innovative Deep Neural Network Archite...

  Transformers For Natural Language Processing - Build Innovative Deep Neural
  Network Architectures For NLP With Python, PyTorch, TensorFlow, BERT, RoBERTa,
  And More
   LANGUAGE: English     ░   ISBN: 9781800565791      ░   REMOVED: DRM
https://www.worldcat.org/search?q=bn%3A9781800565791&fq=x0%3Abook+%2B+x4%3Adigit
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  Become an AI language understanding expert by mastering the quantum leap of
 Transformer neural network models Key Features 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 Book Description 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. What You Will Learn 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 productivity of key transformers to define
 their scope, potential, and limits, in production Who this book is for
 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.

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