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Applied Deep Learning with Keras

You're reading from   Applied Deep Learning with Keras Solve complex real-life problems with the simplicity of Keras

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Product type Paperback
Published in Apr 2019
Publisher
ISBN-13 9781838555078
Length 412 pages
Edition 1st Edition
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Authors (3):
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Matthew Moocarme Matthew Moocarme
Author Profile Icon Matthew Moocarme
Matthew Moocarme
Mahla Abdolahnejad Mahla Abdolahnejad
Author Profile Icon Mahla Abdolahnejad
Mahla Abdolahnejad
Ritesh Bhagwat Ritesh Bhagwat
Author Profile Icon Ritesh Bhagwat
Ritesh Bhagwat
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Toc

Chapter 1. Introduction to Machine Learning with Keras

Note

Learning Objectives

By the end of this chapter, you will be able to:

  • Present data for use in machine learning models

  • Explain how to preprocess data for a machine learning model

  • Build a logistic regression model with scikit-learn

  • Use regularization in machine learning models

  • Evaluate model performance with model evaluation metrics

Note

In this chapter, we will learn how to preprocess data for machine learning models. We will learn how to develop logistic regression models with scikit-learn. Lastly, we will evaluate model performance with model evaluation metrics.

You have been reading a chapter from
Applied Deep Learning with Keras
Published in: Apr 2019
Publisher:
ISBN-13: 9781838555078
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