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Machine Learning Using TensorFlow Cookbook

You're reading from   Machine Learning Using TensorFlow Cookbook Create powerful machine learning algorithms with TensorFlow

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781800208865
Length 416 pages
Edition 1st Edition
Languages
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Authors (3):
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Konrad Banachewicz Konrad Banachewicz
Author Profile Icon Konrad Banachewicz
Konrad Banachewicz
Luca Massaron Luca Massaron
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Luca Massaron
Alexia Audevart Alexia Audevart
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Alexia Audevart
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Table of Contents (15) Chapters Close

Preface 1. Getting Started with TensorFlow 2.x 2. The TensorFlow Way FREE CHAPTER 3. Keras 4. Linear Regression 5. Boosted Trees 6. Neural Networks 7. Predicting with Tabular Data 8. Convolutional Neural Networks 9. Recurrent Neural Networks 10. Transformers 11. Reinforcement Learning with TensorFlow and TF-Agents 12. Taking TensorFlow to Production 13. Other Books You May Enjoy
14. Index

Linear Regression

Linear regression may be one of the most important algorithms in statistics, machine learning, and science in general. It's one of the most widely used algorithms, and it is very important to understand how to implement it and its various flavors. One of the advantages that linear regression has over many other algorithms is that it is very interpretable. We end up with a number (a coefficient) for each feature and such a number directly represents how that feature influences the target (the so-called dependent variable).

For instance, if you had to predict the selling value of a house and you obtained a dataset of historical sales comprising house characteristics (such as the lot size, indicators of the quality and condition of the house, and the distance from the city center), you could easily apply a linear regression. You could obtain a reliable estimator in a few steps and the resulting model would be easy to understand and explain to others, too. A...

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