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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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Toc

Table of Contents (14) Chapters Close

Preface 1. Introduction to Natural Language Processing 2. Understanding TensorFlow FREE CHAPTER 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

Chapter 9. Applications of LSTM – Image Caption Generation

In the previous chapter, we saw how we can use LSTMs to generate text. In this chapter, we will use an LSTM to solve a more complex task: generating suitable captions for given images. This task is more complex in the sense that solving it involves multiple subtasks, such as training/using a CNN to generate encoded vectors of images, learning word embeddings, and training an LSTM to generate captions. So this is not as straightforward as the text generation task, where we simply input text and output text in a sequential manner.

Automated image captioning or image annotation has a wide variety of applications. One of the most prominent application is image retrieval in search engines. Automated image captioning can be used to retrieve all the images belonging to a certain concept (for example, a cat) as per the user's request. Another application can be in social media, where, when an image is uploaded...

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