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Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more
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Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more
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.
Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more
商品＃: 32485870

Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT,

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商品＃: 32485870

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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.
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Shop Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more online at a best price in Taiwan. 1800565798
  • Build and implement state-of-the-art language models using Python and deep learning concepts
  • Explore transformer architectures such as the original transformer, BERT, RoBERTa, and GPT-2
  • Work with pretrained transformer models from tech giants like Google, Facebook, and OpenAI
  • Apply transformers to various NLP domains including machine translation and text summarization
  • Learn how to measure the productivity and limitations of transformer models in production
  • Ideal for experienced deep learning practitioners and data scientists familiar with Python and neural networks
Publisher Packt Publishing
Publication date January 29, 2021
Language English
Print length 384 pages
ISBN-10 1800565798
ISBN-13 978-1800565791
Item Weight 7.4 ounces (209.79 grams)
Dimensions 7.5 x 0.87 x 9.25 inches (19.1 x 2.2 x 23.5 cm)

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Transformers for Natural Language Processing: Build innovative deep neural network architectures for NLP with Python, PyTorch, TensorFlow, BERT, RoBERTa, and more

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Neural Networks Editorial Review

This book on natural language processing (NLP) transformers receives mixed reviews from customers. While some found it to be a great resource for beginners in NLP who want to learn about deep learning and AI, explaining each topic in detail and providing relevant python programs to illustrate key aspects, others found it to be a collection of verbiage made upon easy notebooks, reproducing knowledge proposed for free on HF or AllenNLP websites. The book covers topics like BERT, RoBERTa, superglue, language understanding, translations, and GPT-2 and 3. Some customers found the codes provided to be requiring a lot of modifications to work on Colab and that the book assumes Considerable NLP knowledge. Others found the explanations of key concepts to be terrible and the typesetting of formulas horrible and ambiguous. One customer found the book to be missing the hat, and not being a good introduction to transformers. Another customer found the book to be the best resource they found so far and it could direct their study material and then apply it.

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優點

  • Suitable for beginners in NLP who want to learn about deep learning and AI.
  • Provides relevant python programs to illustrate key aspects.
  • Covers topics like BERT, RoBERTa, superglue, language understanding, translations, and GPT-2 and 3.
  • Explains transformers model in detail with the simplified attention getting as their key to encoding and decoding.

缺點

  • Some customers found it to be a collection of verbiage made upon easy notebooks, reproducing knowledge proposed for free on HF or AllenNLP websites.

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特色和優勢

  • In-depth exploration of the transformer architecture for NLP
  • Covers various eminent models and datasets
  • Grasp advanced language understanding techniques
  • Use pretrained transformer models for various datasets
  • Suitable for readers familiar with neural networks and programming
  • Apply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and more

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