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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 7. Long Short-Term Memory Networks

In this chapter, we will discuss a more advanced RNN variant known as Long Short-Term Memory Networks (LSTMs). LSTMs are widely used in many sequential tasks (including stock market prediction, language modeling, and machine translation) and have proven to perform better than other sequential models (for example, standard RNNs), especially given the availability of large amounts of data. LSTMs are well-designed to avoid the problem of the vanishing gradient that we discussed in the previous chapter.

The main practical limitation posed by the vanishing gradient is that it prevents the model from learning long-term dependencies. However, by avoiding the vanishing gradient problem, LSTMs have the ability to store memory for longer than ordinary RNNs (for hundreds of time steps). In contrast to those RNNs, which only maintain a single hidden state, LSTMs have many more parameters as well as better control over what memory to store and what...

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