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Transfer Learning for Natural Language Processing

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Transfer Learning for Natural Language Processing is a practical primer to transfer learning techniques capable of delivering huge improvements to your NLP models. Written by DARPA researcher Paul Azunre, this practical book gets you up to speed with the relevant ML concepts before diving into the cutting-edge advances that are defining the future of NLP. You’ll learn how to adapt existing state-of-the art models into real-world applications, including building a spam email classifier, a movie review sentiment analyzer, an automated fact checker, a question-answering system and a translation system for low-resource languages. For each NLP application, you’ll learn how to setup a microservices software architecture that will fine-tune your model as new data comes in.

Learn more about the book here: https://www.manning.com/books/transfer-learning-for-natural-language-processing

Publicado en: Software
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Transfer Learning for Natural Language Processing

  1. 1. Improving NLP Model Training Time and Efficiency With Transfer Learning for Natural Language Processing. Take 42% off by entering slazunre into the discount code box at checkout at manning.com.
  2. 2. Building and training deep learning models from scratch is costly, time- consuming, and requires massive amounts of data. To address this concern, cutting-edge transfer learning techniques enable you to start with pretrained models you can tweak to meet your exact needs.
  3. 3. Transfer Learning for Natural Language Processing shows you how to customize open source resources for your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results even when working with limited label data, all while saving on training time and computational costs.
  4. 4. Transfer Learning for Natural Language Processing is a practical primer to transfer learning techniques capable of delivering huge improvements to your NLP models. For several NLP applications, you’ll learn to setup a software architecture that will fine-tune your model as new data comes in.
  5. 5. What people are saying about the book: An interesting book that introduces Transfer Learning techniques in the domain of NLP. -Nikos Kanakaris A complex topic is broken down into manageable pieces, while maintaining a good pace. The text is accompanied by replicable code examples throughout. -Mathijs Affourtit
  6. 6. About the author: Paul Azunre holds a PhD in Computer Science from MIT. He works as a Research Director studying Transfer Learning in NLP. He is active in the field writing peer- reviewed articles, serving as a program committee member and reviewing for top conferences. Paul has also served as a Principal Investigator on several DARPA research programs at the US Department of Defense.
  7. 7. Take 42% off Transfer Learning for Natural Language Processing by entering slazunre into the discount code box at checkout at manning.com. You can check the book our on our browser-based liveBook reader here.

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