Transformers for Natural Language Processing

Transformers for Natural Language Processing

eBook Details:

  • Paperback: 384 pages
  • Publisher: WOW! eBook (February 9, 2021)
  • Language:¬†English
  • ISBN-10: 1800565798
  • ISBN-13: 978-1800565791

eBook Description:

Transformers for Natural Language Processing: Become an AI language understanding expert by mastering the quantum leap of Transformer neural network model

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 in context with the Transformers.

The book takes you through Natural Language Processing (NLP) 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.

  • Use the latest pre-trained 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

By the end of this Transformers for Natural Language Processing 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.

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1 Response

  1. February 5, 2021

    […] Natural Language Processing with TensorFlow 2: One-stop solution for NLP practitioners, ML developers and data scientists to […]

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