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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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Hyperparameter Tuning with scikit-learn


Hyperparameter tuning is a very important technique for improving the performance of deep learning models. In Chapter 4, Evaluating your Model with Cross Validation with Keras Wrappers, you learned about using a Keras wrapper with scikit-learn, which allows for Keras models to be used in a scikit-learn workflow. As a result, different general machine learning and data analysis tools and methods available in scikit-learn can be applied to Keras deep learning models. Among those methods are scikit-learn hyperparameter optimizers. In the previous chapter, you learned how to perform hyperparameter tuning by writing user-defined functions to loop over possible values for each hyperparameter. In this section, you will learn how to perform it in a much easier way by using various hyperparameter optimization methods available in scikit-learn. You will also get to practice applying those methods by completing an activity involving a real-life dataset.

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