February 2019
Intermediate to advanced
260 pages
6h 3m
English
Content preview from Hands-On Java Deep Learning for Computer Vision
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Minimizing the cost function
Our first step will be to generate a noisy random image (G) that looks a bit like the content image (C). The following example on the right is maybe 80% noisy random and it looks 20% like the content image:
Also, we can generate a pure noisy image, and it will work just fine, but it speeds up a bit if we start with a more content-like image. Then, we will use gradient descent (J(G)) exactly as we saw in the first section, and use the following formula:
We will use the feedback, which is the derivation of the cost ...
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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.