Deep Learning for Natural Language Processing

Deep Learning for Natural Language Processing

eBook Details:

  • Paperback: 296 pages
  • Publisher: WOW! eBook; 1st edition (October 12, 2022)
  • Language: English
  • ISBN-10: 1617295442
  • ISBN-13: 978-1617295447

eBook Description:

Deep Learning for Natural Language Processing: Explore the most challenging issues of natural language processing, and learn how to solve them with cutting-edge deep learning!

Deep learning has advanced natural language processing to exciting new levels and powerful new applications! For the first time, computer systems can achieve “human” levels of summarizing, making connections, and other tasks that require comprehension and context. Deep Learning for Natural Language Processing reveals the groundbreaking techniques that make these innovations possible. Stephan Raaijmakers distills his extensive knowledge into useful best practices, real-world applications, and the inner workings of top NLP algorithms.

Deep learning has transformed the field of natural language processing. Neural networks recognize not just words and phrases, but also patterns. Models infer meaning from context, and determine emotional tone. Powerful deep learning-based NLP models open up a goldmine of potential uses.

Inside Deep Learning for Natural Language Processing you’ll find a wealth of NLP insights, including:

  • An overview of NLP and deep learning
  • One-hot text representations
  • Word embeddings
  • Models for textual similarity
  • Sequential NLP
  • Semantic role labeling
  • Deep memory-based NLP
  • Linguistic structure
  • Hyperparameters for deep NLP

Deep Learning for Natural Language Processing teaches you how to create advanced NLP applications using Python and the Keras deep learning library. You’ll learn to use state-of the-art tools and techniques including BERT and XLNET, multitask learning, and deep memory-based NLP. Fascinating examples give you hands-on experience with a variety of real world NLP applications. Plus, the detailed code discussions show you exactly how to adapt each example to your own uses!

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