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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

Predicting house prices

In the previous case study, we had an output that was categorical. In this case study, we shall look into an output that is continuous in nature, by trying to predict the price of a house where 13 variables that are likely to impact the house price are provided as input.

The objective is to minimize the error by which we predict the price of a house.

Getting ready

Given that the objective is to minimize error, let's define the error that we shall be minimizing—we should ensure that a positive error and a negative error do not cancel out each other. Hence, we shall minimize the absolute error. An alternative of this is to minimize the squared error.

Now that we have fine-tuned our objective...

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