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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

Processing categorical data

Strings usually represent categorical data in tabular data. Each unique value in a categorical feature represents a quality that refers to the example we are examining (hence, we consider this information to be qualitative whereas numerical information is quantitative). In statistical terms, each unique value is called a level and the categorical feature is called a factor. Sometimes you can find numeric codes used as categorical (identifiers), when the qualitative information has been previously encoded into numbers, but the way to deal with them doesn't change: the information is in numeric values but it should be treated as categorical.

Since you don't know how each unique value in a categorical feature is related to every other value present in the feature (if you jump ahead and group values together or order them you are basically expressing a hypothesis you have about the data), you can treat each of them as a value in itself. Hence...

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