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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
Author Profile Icon Luca Massaron
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

Using a multilayer neural network

We will now apply our knowledge of different layers to real data by using a multilayer neural network on the low birth weight dataset.

Getting ready

Now that we know how to create neural networks and work with layers, we will apply this methodology with the aim of predicting birth weights in the low birth weight dataset. We'll create a neural network with three hidden layers. The low birth weight dataset includes the actual birth weights and an indicator variable for whether the given birth weight is above or below 2,500 grams. In this example, we'll make the target the actual birth weight (regression) and then see what the accuracy is on the classification at the end. At the end, our model should be able to identify whether the birth weight will be <2,500 grams.

How to do it...

We proceed with the recipe as follows:

  1. We will start by loading the libraries as follows:
    import tensorflow as tf
    import matplotlib...
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