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Natural Language Processing with TensorFlow

Natural Language Processing with TensorFlow

By : Saad, Ganegedara
4.5 (10)
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Natural Language Processing with TensorFlow

Natural Language Processing with TensorFlow

4.5 (10)
By: Saad, Ganegedara

Overview of this book

Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks. Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator. After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks.
Table of Contents (14 chapters)
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13
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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