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The TensorFlow Workshop

You're reading from   The TensorFlow Workshop A hands-on guide to building deep learning models from scratch using real-world datasets

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781800205253
Length 600 pages
Edition 1st Edition
Languages
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Authors (4):
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Matthew Moocarme Matthew Moocarme
Author Profile Icon Matthew Moocarme
Matthew Moocarme
Abhranshu Bagchi Abhranshu Bagchi
Author Profile Icon Abhranshu Bagchi
Abhranshu Bagchi
Anthony Maddalone Anthony Maddalone
Author Profile Icon Anthony Maddalone
Anthony Maddalone
Anthony So Anthony So
Author Profile Icon Anthony So
Anthony So
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Toc

Table of Contents (13) Chapters Close

Preface
1. Introduction to Machine Learning with TensorFlow 2. Loading and Processing Data FREE CHAPTER 3. TensorFlow Development 4. Regression and Classification Models 5. Classification Models 6. Regularization and Hyperparameter Tuning 7. Convolutional Neural Networks 8. Pre-Trained Networks 9. Recurrent Neural Networks 10. Custom TensorFlow Components 11. Generative Models Appendix

2. Loading and Processing Data

Activity 2.01: Loading Tabular Data and Rescaling Numerical Fields with a MinMax Scaler

Solution:

  1. Open a new Jupyter notebook to implement this activity. Save the file as Activity2-01.ipnyb.
  2. In a new Jupyter Notebook cell, import the pandas library, as follows:
    import pandas as pd
  3. Create a new pandas DataFrame named df and read the Bias_correction_ucl.csv file into it. Examine whether your data is properly loaded by printing the resultant DataFrame:
    df = pd.read_csv('Bias_correction_ucl.csv')

    Note

    Make sure you change the path (highlighted) to the CSV file based on its location on your system. If you're running the Jupyter notebook from the same directory where the CSV file is stored, you can run the preceding code without any modification.

  4. Drop the date column using the drop method. Since you're dropping the columns, pass 1 to the axis argument and True to the inplace argument:
    df.drop(&apos...
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