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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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 Saad Saad
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Saad
 Ganegedara Ganegedara
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Ganegedara
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Table of Contents (16) Chapters Close

Natural Language Processing with TensorFlow
Contributors
Preface
1. Introduction to Natural Language Processing FREE CHAPTER 2. Understanding TensorFlow 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 Mathematical Foundations and Advanced TensorFlow Index

Other variants of LSTMs


Though we mainly focus on the standard LSTM architecture, many variants have emerged that either simplify the complex architecture found in standard LSTMs or produce better performance or both. We will look at two variants that introduce structural modifications to the cell architecture of LSTM: peephole connections and GRUs.

Peephole connections

Peephole connections allow gates not only to see the current input and the previous final hidden state but also the previous cell state. This increases the number of weights in the LSTM cell. Having such connections have shown to produce better results. The equations would look like these:

Let's briefly look at how this helps the LSTM perform better. So far, the gates see the current input and final hidden state, but not the cell state. However, in this configuration, if the output gate is close to zero, even when the cell state contains important information crucial for better performance, the final hidden state will be close...

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