January 2018
Beginner to intermediate
284 pages
8h 35m
English
Content preview from Deep Learning Essentials
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The difference between hard attention and soft attention
There are two types of attention models: hard attention and soft attention.
In hard attention, each part in a sentence or patch in an image is either used to obtain the context vector or is discarded. In this case, αti represents the probability of the part/patch being used; that is, the probability of indicator St, i = 1. For example, in Xu’s Show, Attend and Tell, the context vector in the hard attention senario is computed as:
Given the sum-to-one criterion, it can be seen clearly that hard attention selects the element with the highest probability. This is definitely more philosophically ...
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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.