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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

Understanding the Architecture of a CNN

Let's assume we have the task of classifying each of the MNIST images as a number between 0 and 9. The input in the previous example is an image matrix. For a colored image, each pixel is an array with three values corresponding to the RGB color scheme. For grayscale images, each pixel is just one number, as we saw earlier.

To understand the architecture of a CNN, it is best to separate it into two sections as visualized in the image that follows.

A forward pass of the CNN involves a set of operations in the two sections.

Figure 4.4: Application of convolution and ReLU operations
Figure 4.4: Application of convolution and ReLU operations

The figure is explained in the following sections:

  • Feature extraction
  • Neural network

Feature Extraction

The first section of a CNN is all about feature extraction. Conceptually, it can be interpreted as the model's attempt to learn which features distinguish one class from another. In the task of classifying images, these features might...

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