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Hands-On Automated Machine Learning
book

Hands-On Automated Machine Learning

by Sibanjan Das, Umit Mert Cakmak
April 2018
Beginner to intermediate content levelBeginner to intermediate
282 pages
6h 52m
English
Packt Publishing
Content preview from Hands-On Automated Machine Learning

The pooling layer

The pooling layer further reduces the size of the feature representation by applying a pooling function. There are different kinds of pooling functions, such as average, min, and max. Max pooling is widely used as it tends to keep the max values of a feature map for each stride.  This is similar to the convolution layer where we have a sliding window and the window slides over the feature map to find the max value within each stride. The window size in a pooling layer is typically less than that used in the convolution layer.

The pooled feature map is then flattened to a 1D representation to be used in a fully connected layer.

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Publisher Resources

ISBN: 9781788629898Supplemental Content