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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

Designing a Neural Network and Its Applications

Common machine learning techniques are used when training and designing a neural network. Neural networks can be classified as:

  • Supervised neural networks
  • Unsupervised neural networks

Supervised neural networks

These are like the example used in the previous section (predicting the price of the house based on how many rooms it has). Supervised neural networks are trained on datasets consisting of sample inputs with their corresponding outputs. These are suitable for noise classification and making predictions.

There are two types of supervised learning methods:

  • Classification

    This is for problems that have discrete categories or classes as target outputs, for example the Iris dataset. The neural network learns from sample inputs and outputs how to correctly classify new data.

  • Regression

    This is for problems that have a range of continuous numerical values as target outputs, like the price of a house example. The neural network...

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