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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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Summary


In this chapter, you learned about cross-validation, which is one of the most important resampling methods. It results in the best estimation of model performance on independent data. You learned about the basics of cross-validation and its two different variations, along with a comparison of them. You also learned about the Keras wrapper with scikit-learn, which is a very helpful tool that allows scikit-learn methods and functions such as performing cross-validation to be easily applied to Keras models. You learned the step-by-step process of implementing cross-validation in order to evaluate Keras deep learning models using the Keras wrapper with scikit-learn. Lastly, you learned that cross-validation estimations of model performance can be used to decide among different models for a particular problem or to decide about parameters (or hyperparameters) for a particular model. You practiced using cross-validation for this purpose by writing user-defined functions in order to perform...

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