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Neural network methods for natural language processing / Yoav Goldberg

By: Material type: TextTextLanguage: English Publication details: Claypool : Springer International Publishing , 2012.Description: xxii, 287 pages : illustrations ; 24 cmISBN:
  • 9783031010378
Subject(s): DDC classification:
  • 006.35 GOL
Contents:
Supervised Classification and Feed-forward Neural Networks -- Working with Natural Language Data -- Specialized Architectures -- Additional Topics.
Summary: Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries.
List(s) this item appears in: New Arrivals - February 1st to 28th 2026
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Item type Current library Call number Status Barcode
Books Institute of Public Enterprise, Library S Campus 006.35 GOL (Browse shelf(Opens below)) Available 50973
Books Institute of Public Enterprise, Library S Campus 006.35 GOL (Browse shelf(Opens below)) Available 50974
Books Institute of Public Enterprise, Library S Campus 006.35 GOL (Browse shelf(Opens below)) Available 50975

Supervised Classification and Feed-forward Neural Networks -- Working with Natural Language Data -- Specialized Architectures -- Additional Topics.

Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries.

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