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Deep Learning with PyTorch

You're reading from   Deep Learning with PyTorch A practical approach to building neural network models using PyTorch

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781788624336
Length 262 pages
Edition 1st Edition
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Author (1):
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Vishnu Subramanian Vishnu Subramanian
Author Profile Icon Vishnu Subramanian
Vishnu Subramanian
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Table of Contents (11) Chapters Close

Preface 1. Getting Started with Deep Learning Using PyTorch FREE CHAPTER 2. Building Blocks of Neural Networks 3. Diving Deep into Neural Networks 4. Fundamentals of Machine Learning 5. Deep Learning for Computer Vision 6. Deep Learning with Sequence Data and Text 7. Generative Networks 8. Modern Network Architectures 9. What Next? 10. Other Books You May Enjoy

Language modeling

We will learn how to teach a recurrent neural network (RNN) how it can create a sequence of text. In simple words, the RNN model that we will build now will be able to predict the next word, given some context. This is just like the Swift app on your phone, which guesses the next word that you are typing. The ability to generate sequential data has applications in many different areas, such as:

  • Image captioning
  • Speech recognition
  • Language translation
  • Automatic email reply

We learnt in Chapter 6, Deep Learning with Sequence Data and Text, that RNNs are tough to train. So, we will be using a variant of RNN called Long Short-Term Memory (LSTM). The development of the LSTM algorithm started in 1997 but became popular in the last few years. It became popular due to the availability of powerful hardware and quality data, and some advancements such as dropout also...

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