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Deep Learning for Natural Language Processing

You're reading from   Deep Learning for Natural Language Processing Solve your natural language processing problems with smart deep neural networks

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Product type Paperback
Published in Jun 2019
Publisher
ISBN-13 9781838550295
Length 372 pages
Edition 1st Edition
Languages
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Authors (4):
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Karthiek Reddy Bokka Karthiek Reddy Bokka
Author Profile Icon Karthiek Reddy Bokka
Karthiek Reddy Bokka
Monicah Wambugu Monicah Wambugu
Author Profile Icon Monicah Wambugu
Monicah Wambugu
Tanuj Jain Tanuj Jain
Author Profile Icon Tanuj Jain
Tanuj Jain
Shubhangi Hora Shubhangi Hora
Author Profile Icon Shubhangi Hora
Shubhangi Hora
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Toc

Table of Contents (11) Chapters Close

About the Book 1. Introduction to Natural Language Processing FREE CHAPTER 2. Applications of Natural Language Processing 3. Introduction to Neural Networks 4. Foundations of Convolutional Neural Network 5. Recurrent Neural Networks 6. Gated Recurrent Units (GRUs) 7. Long Short-Term Memory (LSTM) 8. State-of-the-Art Natural Language Processing 9. A Practical NLP Project Workflow in an Organization 1. Appendix

Introduction

We encounter different kinds of data in our day-to-day lives, and some of this data has temporal dependencies (dependencies over time) while some does not. For example, an image by itself contains the information it wants to convey. However, data forms such as audio and video have dependencies over time. They cannot convey information if a fixed point in time is taken into consideration. Based on the problem statement, the input that's needed in order to solve the problem can differ. If we have a model to detect a particular person in a frame, a single image can be used as input. However, if we need to detect their actions, we need a stream of images, contiguous in time, as the input. We can understand the person's actions by analyzing these images together, but not independently.

While watching a movie, a particular scene makes sense because its context is known, and we remember all the information gathered before in the movie to understand the current scene. This...

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