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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 FREE CHAPTER 2. Building a Deep Feedforward Neural Network 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

Leveraging a functional API

In this section, we will continue to improve the accuracy of the stock price prediction by integrating historical price points data with the most-recent headlines of the company for which we are predicting the stock price.

The strategy that we will adopt to integrate data from multiple sources—structured (historical price) data and unstructured (headline) data is as follows:

  • We will convert the unstructured text into a structured format in a manner that is similar to the way we categorized news articles into topics.
  • We will pass the structured format of text through a neural network and extract the hidden layer output.
  • Finally, we pass the hidden layer output to the output layer, where the output layer has one node.
  • In a similar manner, we pass the input historical price data through the neural network to extract the hidden layer values, which...
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