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TensorFlow 1.x Deep Learning Cookbook

You're reading from TensorFlow 1.x Deep Learning Cookbook

Product type Book

Published in Dec 2017

Publisher Packt

ISBN-13 9781788293594

Pages 536 pages

Edition 1st Edition

Languages

Concepts

Table of Contents (15) Chapters

Preface

1. TensorFlow - An Introduction

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

Components of TPUs

House price estimation-simple linear regression

In this recipe, we will perform simple linear regression based on the number of rooms (RM) on the Boston house price dataset.

Getting ready

Our goal is to predict the house price given in the last column (MEDV). In this recipe, we load the dataset from the TensorFlow Contrib dataset directly. We optimize the coefficients for an individual training sample using the stochastic gradient descent optimizer.

How to do it...

We proceed with the recipe as follows:

The first step is to import all the packages that we will need:

...

The rest of the chapter is locked

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