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Neural Networks with Keras Cookbook

You're reading from   Neural Networks with Keras Cookbook Over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789346640
Length 568 pages
Edition 1st Edition
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Authors (2):
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V Kishore Ayyadevara V Kishore Ayyadevara
Author Profile Icon V Kishore Ayyadevara
V Kishore Ayyadevara
Srinivas Pradeep Srinivas Pradeep
Author Profile Icon Srinivas Pradeep
Srinivas Pradeep
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Toc

Table of Contents (18) Chapters Close

Preface 1. Building a Feedforward Neural Network 2. Building a Deep Feedforward Neural Network FREE CHAPTER 3. Applications of Deep Feedforward Neural Networks 4. Building a Deep Convolutional Neural Network 5. Transfer Learning 6. Detecting and Localizing Objects in Images 7. Image Analysis Applications in Self-Driving Cars 8. Image Generation 9. Encoding Inputs 10. Text Analysis Using Word Vectors 11. Building a Recurrent Neural Network 12. Applications of a Many-to-One Architecture RNN 13. Sequence-to-Sequence Learning 14. End-to-End Learning 15. Audio Analysis 16. Reinforcement Learning 17. Other Books You May Enjoy

Building a word vector using the skip-gram and CBOW models

In the previous recipe, we built a word vector. In this recipe, we'll build skip-gram and CBOW models using the gensim library.

Getting ready

The method that we have adopted to build a word vector in this recipe is called a continuous bag of words (CBOW) model. The reason it is called as CBOW is explained as follows:

Let's use this sentence as an example: I enjoy playing TT.

Here's how the CBOW model handles this sentence:

  1. Fix a window of certain size—let's say 1.
    • By specifying the window size, we are specifying the number of words that will be considered to the right as well as to the left of the given word.
  2. Given the window size, the...
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