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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

Summary

In this chapter, we were introduced to a subset of machine learning—deep learning. You learned about the differences and similarities between the two categories of techniques and understood the requirement for deep learning and its applications.

Neural networks are artificial representations of the biological neural networks that are present in the human brain. Artificial neural networks are frameworks that are incorporated by deep learning models and have proven to be increasingly efficient and accurate. They are used in several fields, from training self-driving cars to detecting cancer cells in very early stages.

We studied the different components of a neural network and learned a network trains and corrects itself, with the help of the loss function, the gradient descent algorithm and backpropagation. You also learned how to perform sentiment analysis on text inputs! Furthermore, you learned the basics of deploying a model as a service.

In the coming chapters, you will learn...

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