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TensorFlow 1.x Deep Learning Cookbook

You're reading from   TensorFlow 1.x Deep Learning Cookbook Over 90 unique recipes to solve artificial-intelligence driven problems with Python

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Product type Paperback
Published in Dec 2017
Publisher Packt
ISBN-13 9781788293594
Length 536 pages
Edition 1st Edition
Languages
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Authors (2):
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Dr. Amita Kapoor Dr. Amita Kapoor
Author Profile Icon Dr. Amita Kapoor
Dr. Amita Kapoor
Antonio Gulli Antonio Gulli
Author Profile Icon Antonio Gulli
Antonio Gulli
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Toc

Table of Contents (15) Chapters Close

Preface 1. TensorFlow - An Introduction FREE CHAPTER 2. Regression 3. Neural Networks - Perceptron 4. Convolutional Neural Networks 5. Advanced Convolutional Neural Networks 6. Recurrent Neural Networks 7. Unsupervised Learning 8. Autoencoders 9. Reinforcement Learning 10. Mobile Computation 11. Generative Models and CapsNet 12. Distributed TensorFlow and Cloud Deep Learning 13. Learning to Learn with AutoML (Meta-Learning) 14. TensorFlow Processing Units

House price estimation-multiple linear regression

We can do multiple linear regression on the same data by making a slight modification in the declaration of weights and placeholders. In the case of multiple linear regression, as each feature has different value ranges, normalization becomes essential. Here is the code for multiple linear regression on the Boston house price dataset, using all the 13 input features.

How to do it...

Here is how we proceed with the recipe:

  1. The first step is to import all the packages that we will need:
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
  1. We need to normalize the feature data since the data ranges of all the features are varied. We define a normalize...
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