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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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Toc

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

Predicting with Tabular Data

Most of the available data that can be easily found is not composed of images or text documents, but it is instead made of relational tables, each one possibly containing numbers, dates, and short text, which can be all joined together. This is because of the widespread adoption of database applications based on the relational paradigm (data tables that can be combined together by the values of certain columns that act as joining keys). These tables are the main source of tabular data nowadays and because of that, there are certain challenges.

Here are the challenges commonly faced by Deep Neural Networks (DNNs) when applied to tabular data:

  • Mixed features data types
  • Data in a sparse format (there are more zeros than non-zero data), which is not the best for a DNN converging to an optimum solution
  • No state-of-the-art architecture has emerged yet, there are just some various best practices
  • Less data is available...
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