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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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Cross-Validation for Deep Learning Models


In this section, you will learn about using the Keras wrapper with scikit-learn, a very helpful tool that allows us to use Keras models as part of a scikit-learn workflow. As a result, scikit-learn methods and functions, such as the one for performing cross-validation, can be easily applied to Keras models. You will learn, step-by-step, how to implement what you learned about cross-validation in the previous section using scikit-learn. Furthermore, you will learn to use cross-validation in order to evaluate Keras deep learning models using the Keras wrapper with scikit-learn. Lastly, you will practice what you learned on a problem involving a real dataset.

Keras Wrapper with scikit-learn

When it comes to general machine learning and data analysis, the scikit-learn library is much richer and easier to use than Keras. That is why being able to use scikit-learn methods on Keras models will be of great value.

Fortunately, Keras comes with a helpful wrapper...

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