Transformers for Natural Language Processing - Second Edition: Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4

Denis Rothman Transformers for Natural Language Processing - Second Edition Reviews Summary

Ratings Breakdown

Rated 4.1 by 120 people

Pros from Reviews

  • In-depth explanations
  • Visual aids provided
  • Good mix of theory and application
  • Expert author
  • Hands-on coding experience
  • Comprehensive content
  • Encourages self-sufficiency
  • Mentorship feel

Cons from Reviews

  • Advanced for beginners
  • Superficial introduction
  • Poor paper quality
  • Lacks rigor
  • Repetitive content
  • Content perceived as hype
  • Limited practical examples
  • Not enough depth

Notable Features

Deep Learning
NLP Techniques
Model Interpretability
Semantic Role Labeling
Text Summarization
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