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Natural Language Processing with TensorFlow

You're reading from   Natural Language Processing with TensorFlow The definitive NLP book to implement the most sought-after machine learning models and tasks

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Product type Paperback
Published in Jul 2022
Publisher Packt
ISBN-13 9781838641351
Length 514 pages
Edition 2nd Edition
Languages
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Author (1):
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Thushan Ganegedara Thushan Ganegedara
Author Profile Icon Thushan Ganegedara
Thushan Ganegedara
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Table of Contents (15) Chapters Close

Preface 1. Introduction to Natural Language Processing FREE CHAPTER 2. Understanding TensorFlow 2 3. Word2vec – Learning Word Embeddings 4. Advanced Word Vector Algorithms 5. Sentence Classification with Convolutional Neural Networks 6. Recurrent Neural Networks 7. Understanding Long Short-Term Memory Networks 8. Applications of LSTM – Generating Text 9. Sequence-to-Sequence Learning – Neural Machine Translation 10. Transformers 11. Image Captioning with Transformers 12. Other Books You May Enjoy
13. Index
Appendix A: Mathematical Foundations and Advanced TensorFlow

Other variants of LSTMs

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

Peephole connections

Peephole connections allow gates to see not only 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 has been 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 information crucial...

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