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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

Applications of LSTM – Generating Text

Now that we have a good understanding of the underlying mechanisms of LSTMs, such as how they solve the problem of the vanishing gradient and update rules, we can look at how to use them in NLP tasks. LSTMs are employed for tasks such as text generation and image caption generation. For example, language modeling is at the core of any NLP task, as the ability to model language effectively leads to effective language understanding. Therefore, this is typically used for pretraining downstream decision support NLP models. By itself, language modeling can be used to generate songs (https://towardsdatascience.com/generating-drake-rap-lyrics-using-language-models-and-lstms-8725d71b1b12), movie scripts (https://builtin.com/media-gaming/ai-movie-script), etc.

The application that we will cover in this chapter is building an LSTM that can write new folk stories. For this task, we will download translations of some folk stories by the Grimm...

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