December 2018
Beginner to intermediate
226 pages
7h 59m
English
Content preview from Hands-On Meta Learning with Python
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Sharpening
The final step is called sharpening. As a result of convolutional shift, the weights ,
, will not be sharp, —in other words, because of the shift, weights focused at a single location will be dispersed into other locations. To mitigate this effect, we perform sharpening. We use a new parameter called 
, which should be greater than or equal to 1 to perform sharpening, and can be expressed as follows:
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.