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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 6. Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are a special family of neural networks that are designed to cope with sequential data (that is, time series data), such as a sequence of texts (for example, variable length sentence or a document) or stock market prices. RNNs maintain a state variable that captures the various patterns present in sequential data; therefore, they are able to model sequential data. For example, conventional feed-forward neural networks do not have this ability unless the data is represented with a feature representation that captures the important patterns present in the sequence. However, coming up with such feature representations is extremely difficult. Another alternative for feed-forward models to model sequential data is to have a separate set of parameters for each position in time/sequence. So that the set of parameters assigned to a certain position learns about the patterns that occur at that position. This will greatly...

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