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Machine Learning for Developers

You're reading from   Machine Learning for Developers Uplift your regular applications with the power of statistics, analytics, and machine learning

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781786469878
Length 270 pages
Edition 1st Edition
Languages
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Authors (2):
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Md Mahmudul Hasan Md Mahmudul Hasan
Author Profile Icon Md Mahmudul Hasan
Md Mahmudul Hasan
Rodolfo Bonnin Rodolfo Bonnin
Author Profile Icon Rodolfo Bonnin
Rodolfo Bonnin
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction - Machine Learning and Statistical Science 2. The Learning Process FREE CHAPTER 3. Clustering 4. Linear and Logistic Regression 5. Neural Networks 6. Convolutional Neural Networks 7. Recurrent Neural Networks 8. Recent Models and Developments 9. Software Installation and Configuration

Univariate time series prediction with energy consumption data

In this example, we will be solving a problem in the domain of regression. For this reason, we will build a multi-layer RNN with two LSTMs. The type of regression we will do is of the many to one type, because the network will receive a sequence of energy consumption values and will try to output the next value based on the previous four registers.

The dataset we will be working on is a compendium of many measurements of the power consumption of one home over a period of time. As we might infer, this kind of behavior can easily follow patterns (it increases when the occupants use the microwave to prepare breakfast and use computers during the day, it decreases a bit in the afternoon, and then increases in the evening with all the lights, finally decreasing to zero when the occupants are asleep).

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