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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 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 A. Mathematical Foundations and Advanced TensorFlow Index

References

[1] Distributed representations of words and phrases and their compositionality, T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean, Advances in Neural Information Processing Systems,pp. 3111–3119, 2013.

[2] Semi-supervised convolutional neural networks for text categorization via region embedding, Johnson, Rie and Tong Zhang, Advances in Neural Information Processing Systems, pp. 919-927, 2015.

[3] A Generative Word Embedding Model and Its Low Rank Positive Semidefinite Solution, Li, Shaohua, Jun Zhu, and Chunyan Miao, Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1599-1609, 2015.

[4] Learning Word Meta-Embeddings, Wenpeng Yin and Hinrich Schütze, Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, vol. 1, pp. 1351-1360, 2016.

[5] Topical Word Embeddings, Yang Liu, Zhiyuan Liu, Tat-Seng Chua, and Maosong Sun, AAAI, pp. 2418-2424, 2015.

[6] Effective Approaches to Attention-based...

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