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Neural network methods in natural language processing

By: Series: Synthesis Lectures on Human Language TechnologiesPublication details: New york : Springer, ©2022Description: 287 p. : col. ill. ; 23 cmISBN:
  • 9783031010378
Subject(s): DDC classification:
  • 006.3 GOL-N
Contents:
Learning Basics and Linear Models From Linear Models to Multi-layer Perceptrons Feed-forward Neural Networks Neural Network Training Features for Textual Data Case Studies of NLP Features From Textual Features to Inputs Language Modeling Pre-trained Word Representations Using Word Embeddings Case Study: A Feed-forward Architecture for Sentence Case Study: A Feed-forward Architecture for Sentence Meaning Inference Ngram Detectors: Convolutional Neural Networks Recurrent Neural Networks: Modeling Sequences and Stacks Concrete Recurrent Neural Network Architectures Modeling with Recurrent Networks Conditioned Generation Modeling Trees with Recursive Neural Networks Structured Output Prediction Cascaded, Multi-task and Semi-supervised Learning
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Holdings
Item type Current library Collection Call number Status Notes Date due Barcode Item holds
Books Books IIITD Reference Computer Science and Engineering CB 006.3 GOL-N (Browse shelf(Opens below)) Available DBT Project Grant 012929
Total holds: 0

Learning Basics and Linear Models From Linear Models to Multi-layer Perceptrons Feed-forward Neural Networks Neural Network Training Features for Textual Data Case Studies of NLP Features From Textual Features to Inputs Language Modeling Pre-trained Word Representations Using Word Embeddings Case Study: A Feed-forward Architecture for Sentence Case Study: A Feed-forward Architecture for Sentence Meaning Inference Ngram Detectors: Convolutional Neural Networks Recurrent Neural Networks: Modeling Sequences and Stacks Concrete Recurrent Neural Network Architectures Modeling with Recurrent Networks Conditioned Generation Modeling Trees with Recursive Neural Networks Structured Output Prediction Cascaded, Multi-task and Semi-supervised Learning

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