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Deep Learning with TensorFlow

You're reading from   Deep Learning with TensorFlow Explore neural networks with Python

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781786469786
Length 320 pages
Edition 1st Edition
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Authors (4):
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Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Ahmed Menshawy Ahmed Menshawy
Author Profile Icon Ahmed Menshawy
Ahmed Menshawy
Giancarlo Zaccone Giancarlo Zaccone
Author Profile Icon Giancarlo Zaccone
Giancarlo Zaccone
Fabrizio Milo Fabrizio Milo
Author Profile Icon Fabrizio Milo
Fabrizio Milo
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Toc

Table of Contents (11) Chapters Close

Preface 1. Getting Started with Deep Learning FREE CHAPTER 2. First Look at TensorFlow 3. Using TensorFlow on a Feed-Forward Neural Network 4. TensorFlow on a Convolutional Neural Network 5. Optimizing TensorFlow Autoencoders 6. Recurrent Neural Networks 7. GPU Computing 8. Advanced TensorFlow Programming 9. Advanced Multimedia Programming with TensorFlow 10. Reinforcement Learning

Text prediction

Language computational models based on RNNs are nowadays among the most successful techniques for statistical language modeling. They can be easily applied in a wide range of tasks, including automatic speech recognition and machine translation.

In this section, we'll explore an RNN model on a challenging task of language processing, guessing the next word in a sequence of text.

You'll find a complete reference for this example in the following page:
https://www.tensorflow.org/versions/r0.8/tutorials/recurrent/index.html.

You can download the source code for this example here (official TensorFlow project GitHub page):
https://github.com/tensorflow/models/tree/master/tutorials/rnn/ptb.

The files to download are as follows:

  • ptb_word_lm.py: This file contains code to train the model on the PTB dataset
  • reader.py: This file contains code to read the dataset

Here we just present only the main...

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