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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

Product type Paperback

Published in May 2018

Publisher Packt

ISBN-13 9781788478311

Length 472 pages

Edition 1st Edition

Languages

Processing

Tools

Processing

Concepts

Deep Learning

Authors (2):

Thushan Ganegedara

Motaz Saad

View More author details

Table of Contents (14) Chapters

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

Implementing an NMT from scratch – a German to English translator

Now we will implement an actual neural machine translator. We will be implementing the NMT using raw TensorFlow operations variables. The exercise is available in ch10/neural_machine_translation.ipynb. However, there is a sublibrary in TensorFlow, known as the seq2seq library. You can read more information about seq2seq as well as, learn to implement an NMT with seq2seq in the Appendix, Mathematical Foundations and Advanced TensorFlow.

The reason why we use raw TensorFlow is because, once you learn to implement a machine translator from scratch without using any helper functions, you will be able to quickly learn to use the seq2seq library. Furthermore, online resources are very scarce for learning to implement sequence-to-sequence models using raw TensorFlow. However, there are numerous resources/tutorials on how to use the seq2seq library for machine translation.

Note

TensorFlow provides very informative sequence to sequence...

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Authors (2)

Ganegedara

Ganegedara

Thushan is a seasoned ML practitioner with 4+ years of experience in the industry. Currently he is a senior machine learning engineer at Canva; an Australian startup that founded the online visual design software, Canva, serving millions of customers. His efforts are particularly concentrated in the search and recommendations group working on both visual and textual content. Prior to Canva, Thushan was a senior data scientist at QBE Insurance; an Australian Insurance company. Thushan was developing ML solutions for use-cases related to insurance claims. He also led efforts in developing a Speech2Text pipeline there. He obtained his PhD specializing in machine learning from the University of Sydney in 2018.

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Saad

Saad

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