January 2018
Beginner to intermediate
284 pages
8h 35m
English
Content preview from Deep Learning Essentials
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Clip gradients
In very deep networks, such as recurrent networks (and possibly recursive ones), the gradient can become very small or very large quickly, and the locality assumption of gradient descent breaks down. The solution, first introduced by Mikolov, is to clip gradients to a maximum value, which makes a big difference in RNNs.
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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.