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Natural Language Processing with TensorFlow

You're reading from   Natural Language Processing with TensorFlow Teach language to machines using Python's deep learning library

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Product type Paperback
Published in May 2018
Publisher Packt
ISBN-13 9781788478311
Length 472 pages
Edition 1st Edition
Languages
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Authors (2):
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Thushan Ganegedara Thushan Ganegedara
Author Profile Icon Thushan Ganegedara
Thushan Ganegedara
Motaz Saad Motaz Saad
Author Profile Icon Motaz Saad
Motaz Saad
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Table of Contents (14) Chapters Close

Preface 1. Introduction to Natural Language Processing 2. Understanding TensorFlow FREE CHAPTER 3. Word2vec – Learning Word Embeddings 4. Advanced Word2vec 5. Sentence Classification with Convolutional Neural Networks 6. Recurrent Neural Networks 7. Long Short-Term Memory Networks 8. Applications of LSTM – Generating Text 9. Applications of LSTM – Image Caption Generation 10. Sequence-to-Sequence Learning – Neural Machine Translation 11. Current Trends and the Future of Natural Language Processing A. Mathematical Foundations and Advanced TensorFlow Index

Chapter 6. Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are a special family of neural networks that are designed to cope with sequential data (that is, time series data), such as a sequence of texts (for example, variable length sentence or a document) or stock market prices. RNNs maintain a state variable that captures the various patterns present in sequential data; therefore, they are able to model sequential data. For example, conventional feed-forward neural networks do not have this ability unless the data is represented with a feature representation that captures the important patterns present in the sequence. However, coming up with such feature representations is extremely difficult. Another alternative for feed-forward models to model sequential data is to have a separate set of parameters for each position in time/sequence. So that the set of parameters assigned to a certain position learns about the patterns that occur at that position. This will greatly...

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