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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

Updates and Gradient Flow

The updates can be listed as follows:

  • Adjusting weight matrix Wy
  • Adjusting weight matrix Ws
  • For updating Wx

Adjusting Weight Matrix Wy

The model can be visualized as follows:

Figure 5.18: Back propagation of loss through weight matrix Wy

For Wy, the update is very simple since there are no additional paths or variables between Wy and the error. The matrix can be realized as follows:

Figure 5.19: Expression for weight matrix Wy

Adjusting Weight Matrix Ws

Figure 5.20: Back propagation of loss through weight matrix Ws with respect to S3

We can calculate the partial derivate of error with respect to Ws using the chain rule, as shown in the previous figure. It looks like that is what is needed, but it's important to remember that St is dependent on St-1, and therefore S3 is dependent on S2, so we need to consider S2 also, as shown here:

Figure 5.21: Back propagation of loss through weight matrix...
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