January 2018
Beginner to intermediate
284 pages
8h 35m
English
Content preview from Deep Learning Essentials
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Convolution
Convolution involves a few concepts, such as convolve, stride, and padding.
For two-dimensional images, convolve happens for each of the color channels. Suppose you have a weight matrix and the image (shown as values at each pixel) as shown in the following figure.
The weight matrix (often called kernel or filter) is applied to the image by placing the kernel over the image to be convolved and moving it across the entire image. If the weight matrix moves 1 pixel at a time, it is called a stride of 1. At each placement, the numbers (pixel values) from the original image are multiplied by the number of the weight matrix that is currently aligned preceding to it.
The sum of all these products is divided by the kernel's normalizer. ...
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